Soil washing is a rapid and efficient method for heavy metals removal. In this study, in order to prove the feasibility of leaching remediation technology for remediation of lead-contaminated soil around a smelter, intermittent oscillating washing tests and extensive continuous tests were conducted to remediate Pb contaminated soils. Plant growth tests with wheat (Bainong Aikang 58) were also conducted to investigate the effects of leaching on plant growth and the Pb content of stems and leaves. The results revealed that in soils, Pb mainly exists in the forms of PbCO3, Pb5(PO4)3Cl2, PbSO4, and in small amounts of PbS. The intermittent oscillating washing tests showed that a lower liquidsolid(L/S) ratio and a higher leaching temperature and time were related to increased Pb removal efficiency. The Pb removal efficiency rates in the oscillating washing tests and the extensive continuous tests were 72.50% and 69.94%, respectively. In addition, the chemical and physical characteristics of the soil were basically unchanged after the leaching regimes. The results of the plant growth tests showed that the leaching regimes did not have a negative effect on the survival rate and growth of the wheat, and the heavy metal contents of the stem and leaf were effectively reduced. This study provides a reference for the large-scale remediation of contaminated soils.

In a 1.5°C warmer world, the Northeastern (NE) South America’s ecosystems will experience more severe droughts, associated with decreasing rainfall. The severity of flash drought events based on vegetation and surface soil moisture has not been identified over the Caatinga ecosys-tem. This study aimed to characterize the impact of flash drought events on vegetation response via soil moisture over NE South America during the first two decades of the 2000s. Three drought indices were used to characterize flash droughts: the Standardized Difference Vegetation Index (SDVI) derived from Meteosat Second Generation (MSG), the Standardized Precipitation Index (SPI) from ground-data, and the Surface Soil Moisture (SSM) product-based Soil Moisture and Ocean Salinity (SMOS). Results revealed dramatic impacts of flash drought events on vegetation dynamics that caused abrupt changes in the regional vegetation phenology. The regional patterns of flash drought events in 2012 over NE South America were identified and had a severe impact on its Caatinga-like vegetation-dependent moisture response. In 2012, anomalously long dry spells with negative rainfall anomalies in the non-rainy season and persistent on vege-tation greenness and rapidly decreased soil moisture were prominent, thus identifying NE South America to the impacts of flash drought events. Additionally, the results from the trends analysis of radiance fluxes estimated from the MSG satellites over 18 years revealed that an overall drying trend in the NE South America semiarid ecosystem during the last two decades. Here, flash drought events were identified as the conse-quent rainfall deficiency at SPI-3< −1 for a period of five consecutive weeks or more, which the soil moisture content dropping from the 40th percentile to below the 20th percentile, with the NDVI lower than 0.30 unit. These results could be useful to guide flash-droughts early warning systems in NE South America.

Olive Anthracnose (OA) is the most important fungal disease of olive fruits worldwide. In the context of integrated pest management, the development of predictive models could be used for early diagnosis and control. In the current study, a dataset representing 58 cases (6 locations with 12 olive cultivars) was used to study the relationship between ΟΑ incidence (OAI) and 35 heterogeneous variables, including orchard characteristics, olive fruit parameters, foliar and soil nutrients, soil parameters and soil texture classes. The Random Forest-Recursive Feature Elimination with Cross Validation (RF-RFECV) feature selection method identified Location, Water Content, P, Ca, Mg, Exchangeable Mg, Trace Zn, Trace Cu as possible new indicators associated with OAI. Six different classification algorithms, namely Decision Tree (DT), Gradient Boosting (GB), Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), were developed for predicting conditions leading to OAI >0% and 10%. Hyperparameter optimization using grid search was used to optimize the parameters of the models and finally the best parameters were applied to predict the OAI. The final models were evaluated in terms of several standard metrics, such as accuracy, sensitivity, specificity and ROC AUC score. Findings suggested that GB performance was superior compared to the other models for the prediction of the occurrence of OA disease (OAI>0%) with an accuracy of 86.7%, a sensitivity of 100%, a specificity of 75% and a ROC-AUC score of 93%, while for the prediction of the spread of the disease (OAI>10%), DT stood out with an accuracy of 86.7%, a sensitivity of 81.8%, a specificity of 100% and a ROC-AUC score of 91%. RF classifier performed very well in both cases, with an accuracy of 80%, a sensitivity of 85.7%, a specificity of 75% and a ROC-AUC score of 93% for the prediction of the occurrence of the disease (OAI>0%), and an accuracy of 86.7%, a sensitivity of 90.9%, a specificity of 75% and a ROC-AUC score of 84% for the prediction of the spread of the disease (OAI>10%).

In order to effectively utilize red mud, an aluminum industrial waste, the stabilized soil material was developed using nano-SiO2 synergistically modified red mud, and the strength characteristics of the stabilized soil were analyzed to clarify the feasibility of such stabilized soil serves as a road base material. Through different combination schemes, the effects of different nano-SiO2 and cement contents on the strength of the stabilized materials were explored. The test results show that: in the synergistic modification of nano-SiO2 and cement, nano-SiO2 can significantly improve the early unconfined compressive strength of red mud-based stabilized soil; in the synergistic modification of nano-SiO2, gypsum and cement, the 7-d unconfined compressive strength of red mud-based stabilized soil is greater than 2 MPa, which meets the strength requirements of road base materials and shows the superiority of synergism. The nominal stress-strain curves are divided into five stages: compressed and compacted stage, elastic deformation stage, plastic deformation stage, damage deformation stage and residual deformation stage. The macroscopic compressive damage pattern of the specimens shows that the modified red mud-based stabilized soil mostly exhibits brittle damage.

A frequent problem in Geotechnics is the soils with inadequate physical-mechanical properties before the efforts to which they will be subjected by the constructions, incurring cost overruns caused by their engineering improvement. The need to improve the engineering properties of soils is not recent, currently observing that the most common alternatives are binders such as cement and lime. The climate change observed in recent decades and the uncontrolled emission of greenhouse gases have motivated Geotechnical and Geoenvironmental researchers to seek mechanisms for soil reinforcement from a more sustainable and environmentally friendly approach by proposing the use of recycled and waste materials. An alternative is natural fibers, which can be obtained as waste from many agro-industrial processes, implying their high availability and low cost. Sawdust as a by-product of wood processing has a rough texture that can generate high friction between the fiber and the matrix of the soils, leading to a significant increase in its shearing strength and bearing capacity. This concept of improving the properties of soils using natural fibers distributed randomly is inspired by the natural phenomenon of grass and/or plants that when growing on a slope can effectively stabilize a said slope.

For embankment slope stability, soil cohesivity is one of the most important shear strength parameters. The effect of cohesiveness on the slope stability of a homogeneous embankment dam under rapid drawdown loading conditions was examined in this study. With the use of numerical modeling in GeoStudio, different situations were explored based on cohesiveness (0 kN/m², 5 kN/m², 10 kN/m^2, and 15 kN/m²) under a 1 m per day drawdown rate. The factor of safety value obtained from the long-term steady-state condition under 15 kN/m² cohesion was equivalent to a 116.8% increase from the one obtained under 0 kN/m² cohesion. The factor of safety values obtained after subjecting the embankment to different soil cohesion levels yielded a p-value of 1.91 x 10^-41, according to the Analysis of Variance. The calculated p-value (alpha value) is less than 0.05, suggesting that the differences between the examined cohesiveness values based on the list of the factor of safety values are statistically significant. The findings derived in this study show that it is significant to capture the effect of material characteristics during the design phase of an embankment dam.

Reaching the land-related UN-Sustainable Development Goals (SDGs) and similar goals articulated by the EU-Green Deal (GD) by 2030 presents a major challenge and requires a pragmatic approach to be focused on joint learning by land users (mostly farmers), researchers and other stakeholders in “Living Labs” and system experiments at experimental farms of research organizations. Defining specific indicators and thresholds for ecosystem services in line with land-related SDGs, are crucial to establish: “Lighthouses” that can act as inspiring examples if they meet the various thresholds. This exploratory paper discusses indicators and thresholds for an arable farm operating on marine, calcareous light clay soils in the Netherlands. Studies of a system experiment are used to discuss and test operational methodology to be widely applied when characterizing many “Living Labs” in future as planned by the European Union. The important role of soils, contributing to ecosystem services, is discussed in terms of soil health. Recommendations are made for innovative methodology to be associated with all land-related SDGs. Satisfying thresholds of ecosystem services, that will vary by soil type, region and farm-type, can be the basis for farm subsidies such as the Common Agricultural Policy (CAP). Research on Living Labs and in system experiments has to be judged by different criteria than those associated with traditional linear research. Important contributions by soils to achieve ecosystem services are framed in terms of soil health and are the most effective way to promote soil science in a by now widely desired inter- and transdisciplinary context.

Droplet digital polymerase chain reaction (ddPCR) is a method used to detect and quantify nu-cleic acids even when present in exceptionally low numbers. While it has proven to be valuable for clinical studies, it has failed to be widely adopted for environmental and applied studies. Due to the complexity of the chemical and biological composition of environmental samples, protocols tailored to clinical studies are not appropriate, and results are difficult to interpret. We used en-vironmental DNA samples originating from field studies to determine a protocol for environ-mental samples. Samples included field soils which had been inoculated with the soil fungus Rhizophagus irregularis (environmental positive control), field soils that had not been inoculat-ed and the targeted fungus was not naturally present (environmental negative control), and root samples from both field categories. To control for the effect of soil inhibitors, we also in-cluded DNA samples of an organismal control extracted from pure fungal spores (organismal positive control). Finally, we included a no-template control consisting only of the PCR reaction reagents and nuclease free water instead of template DNA. Using original data, we examined which factors contribute to poor resolution in root and soil samples and propose best practises to ensure accuracy and repeatability. Furthermore, we evaluated manual and automatic threshold determination methods and we propose a novel protocol based on multiple controls that is more appropriate for environmental samples.

Soils harbor diverse bacteria, and these bacteria play important roles in soil nutrition cycling and carbon storage. Numerous investigations of soil microbiota had been performed, and the core microbiota in different soil or vegetation soil types had been described. However, the complexity of soil environments and relatively limited information of many geographic areas had attracted great attention on comprehensive exploration of soil microbes in enormous types of soil. To reveal the core soil microbiota in Huabei plain, soil samples from metropolis and countryside regions in the Huabei plain were investigated using high-throughput sequencing strategy. The results showed that the most dominant bacteria are Proteobacteria (38.34%), Actinobacteria (20.56%), and Acidobacteria (15.18%). At the genus-level, the most abundant known genera are Gaiella (3.66%), Sphingomonas (3.6%), Acidobacteria Gp6 (2.1%), and Arthrobacter (2%). Moreover, several dominant operational taxanomy units (OTU), such as OTU_3 and OTU_17, were identified to be associated with the soil environment. Microbial distributions of the metropolis samples were different from the countryside samples, which may reflect the environments in the countryside were more diverse than in the metropolis. Microbial diversity and evenness were higher in the metropolis than in the countryside, which might due to the fact that human activity increased the microbial diversity in the metropolis. The soil core microbiota of the Huabei plain were complex, and microbial distributions in the Huabei plain might be mainly affected by the human activity and environmental factors, not by the distance. Our data highlights the soil core microbiota in Huabei Plain, and provides insights for future soil microbiota distribution studies in central China.

Incidental P losses from non-point sources may contribute to eutrophication and to decreased soil fertility. These incidents have been related to heavy rainfall on freshly fertilized agricultural soils and little is known about such incidents on more natural soils or in forests. The aim of this work is to determine if incidents of high P leaching also occur in spruce forests, and if such incidents are of significance in P cycling. We found a peak in the mineral soil solution showing that single events of high P leaching occur. The orthophosphate concentration in the Bf-horizon of the 80-year old spruce forest peaked in the autumn of the second year of a continuous monitoring. The concentration increased by more than 85 times compared to the highest concentration obtained earlier during the sampling. The amount leached during this 6 months peak is 10 times higher than the average annual leaching. This P leaching might be due to a combination of high P deposition/through-fall and a high anion exchange with dissolved-organic-carbon and Cl-. We suggest that single events of high sub-surface P leaching may contribute to the overall P leaching, and might increase with the global warming as more DOC is expected to be released to the soil solution.

The main objective of the present study was to investigate arsenate [As (V)] resistance genes in rice cultivars grown in arsenic contaminated Egyptian soil in order to genetically induce resistance against arsenic in the local rice varieties as well as defining contaminated rice grains and/or soil. Three local rice cultivars; Sakha 102-104 were cultivated on modified Murashige and Skoog Basal Medium (MS medium) containing elevated concentrations of arsenate (0.1, 1 and 10 mg/l). The three varieties showed different resistant attitudes against arsenate with Sakha 104 being the most resistant. Extracted messenger RNA (mRNA) from treated and untreated Sakha 104 plantlets was scanned using differential display to demonstrate the arsenate resistant genes using three different arbitrary primers. About 100 different RNAs with (1500 bp - 50 bp) were obtained from which seven were up-regulated genes, subjected to DNA cloning using TOPO TA system and the selected clones were sequenced. The sequence analysis described four genes out of the seven namely disease resistance protein RPM1, Epstein-Barr virus EBNA-1-like, CwfJ family protein and outer membrane lipoprotein OmlA while the other three genes were hypothetical proteins. It is concluded the four induced genes in the resistant rice cultivar considered as a direct response to arsenic soil pollution. Genes detected in the present study can be used as geno-sensors for rice grains and soil contamination with As (V). Moreover, local rice cultivars may be genetically modified with such genes to induce high resistance and to overcome arsenic soil pollution.

We propose a silviculture with a purely biological logic that takes soil into account. The article is divided into three parts: 1) presentation of the issue in the context of biodiversity and forest pro-tection; 2) impact of the soil functioning on forestry; 3) possible implementation of soil functioning in forestry practice, to support and protect a non-or minimally anthropized evolution of the forest. The first chapter illustrates the importance of the forest for the future of the planet and our human species, referring to recently published works on the topic of climate warming and biodiversity. The second illustrates the aspects that link soil dynamics to the silvogenetic cycle, focusing on the humipedon (organic and organo-mineral surface horizons, approximately the first 30 cm of forest soil), and more significant carbon storage in old forests since up to 2/3 is found in the soil. In the third chapter we consider the possibility of lengthening the forestry cycle, to allow the return of nutrients to the soil. Theoretical graphs and distribution models of the number of trees as a func-tion of diameter and age are reported, with examples of logging in uneven Alpine forests, with reference to Swiss fir-beech forests published on the Pro-Silva website.

Farmers’ conventional tillage (CT) and residue removal practices in rice-maize systems in South Asia’s Eastern Gangetic Plain (EGP) are input-intensive, costly and soil degradative. We conducted a rice-maize-mungbean (R-M-MB) system experiment with six tillage and three residue management treatments in Bangladesh representing the EGP. Maize yields were significantly (p≤0.05) higher under permanent (PB) or fresh (FB) beds and strip tillage (ST) than CT but no differences in mungbean yields. Rice yields under PB, FB and CT were similar, but significantly higher than under zero or minimum tillage and ST. Yields of all crops increased significantly (p≤0.05) with residue retention compared to no retention. Total system productivity was highest under PB followed by FB and ST. Compared with CT, gross margins in PB, FB and ST increased by 18, 13 and 11%, and soil organic matter (SOM) and total N contents across tillage treatments increased by 11-16% and 12-24%, respectively. After three years, SOM and total N and available P and S contents increased significantly (p≤0.05) by residue retention. Results demonstrate the potential of PB, FB and ST with residue retention, for improving the productivity, profitability and soil health under R-M-MB systems in Bangladesh and similar soils in the EGP.

Among the plant nutrients potassium (K) is one of the vital elements required for plant growth and physiology. Potassium is not only a constituent of plant structure but also plays regulatory function in several biochemical processes related to protein synthesis, carbohydrate metabolism, enzyme activation. There are several physiological processes like stomatal regulation and photosynthesis are dependent on K. In the recent decades K was found to provide abiotic stress tolerance. Under salt stress, K helps in maintaining ion homeostasis and regulation of osmotic balance. Under drought stress condition K regulates the stomatal opening and makes the plants adaptive to water deficit. Many reports provided the notion that K enhances the antioxidant defense in plants and therefore, protects the plants from oxidative stress under various environmental adversities. Also, it provides some cellular signaling alone or in association with other signaling molecules and phytohormones. Although a considerable progress in understanding K-induced abiotic stress tolerance in plants has been achieved the exact molecular mechanisms of such protections are still under research. In this review, we summarized the recent literature on the biological functions of K, its uptake, and translocation and its role in plant abiotic stress tolerance.

The subtropical forest soil plays a pivotal role in terrestrial carbon (C) cycling. Although global warming is expected to influence subtropical soil C cycling, a consensus on its impact on soil C dynamics remains elusive. Given the significant response of soil microbial respiration to temperature in subtropical soils, understanding its behavior under varying temperature regimes is critical for predicting soil organic C (SOC) responses to climate warming. We conducted a short-term laboratory soil warming incubation experiment, sampling both warmed and un-warmed soils from a subtropical plantation in southeastern China, incubating them at 20 °C, 30 °C, and 40 °C. Our aim was to study the SOC mineralization response to increasing temperatures. Our findings revealed that the temperature sensitivity (Q10) of SOC mineralization to short-term experimental warming varied between the warmed soil and the un-warmed soil. The Q10 of the un-warmed soil escalated with the temperature treatment (20-30 ℃: 1.31, 30-40℃: 1.63). Conversely, the Q10 of the warmed soil decreased (20-30 ℃:1.57, 30-40 ℃:1.41). Increasing temperature treatments enhanced hydrolytic enzyme activity but decreased soil substrate availability in both un-warmed and warmed soil. The Q10 of un-warmed soil was positively correlated with the response ratio of DOC. Additionally, soil microbial biomass exhibited a significant decline, leading to a reduced total amount of PLFAs and a decreased abundance of bacteria, fungi, and Gram-negative bacteria (GN). The changes in temperature also considerably altered the composition of the warmed soil microbial community. A drop in the microbial quotient coupled with a rise in the metabolic quotient indicated that warming amplified microbial respiration over microbial growth. The differential Q10 of SOC mineralization, in response to temperature across varying soil, can largely be attributed to shifts in soil nutrients, C-degrading enzyme activities, and microbial communities (the ratio of fungal to bacterial PLFAs).

This research explored the influence of spatiotemporal variations in phreatic levels and salinity on the patterns and interactions of soil mesofauna communities in a tropical urban coastal wetland. It delineated significant variations in phreatic levels and salinity, with distinct regimes characterizing different habitats and hydroperiods. A total of 120 mesofauna morphotypes across 42 taxa were identified, revealing habitat differences and similarities, and the ecosystem biodiversity. Moderate correlations were established between phreatic levels, salinity, and mesofauna richness and abundance, with habitat type and phreatic level emerging as significant predictors in the regression model. Our findings indicate that shallow phreatic levels in conjunction with oligohaline salinity conditions foster a more favorable abiotic environment, thereby promoting increased biodiversity. The study underscores the dynamic and adaptive nature of mesofauna communities in response to changing environmental conditions, offering valuable insights for effective costal urban wetland management, particularly in the context of multiple stressors such as global and regional climate change, sea level rise, and human activities. The bio-sensor capacity of soil mesofauna emerges as a vital tool for monitoring and facilitating adaptive ecosystem management, thereby ensuring their long-term health and sustainability.

The research objectives were (1) to examine the determinants of community empowerment towards the revegetation of Arjuna mount forests; (2) assessing the influence of geography and soil conditions on the revegetation of Arjuna mount forests; (3) reviewing the effect of Arjuna mount forest revegetation on the preservation of spring water sources and (4) formulate a model of community empowerment around the forest based on revegetation of Arjuna mount and preservation of the area around the springs. The method used was descriptive method. While based on the techniques and tools used to research, the author uses the survey method to obtain facts that occur in the research area, namely in the area around the spring of the Kedunglarangan and Gumandar Prigen watersheds, Pasuruan Regency. Data obtained in the field are then processed and analyzed using GIS, ArcView 3.3 and Google Earth programs. The study population covering the community around the forest and springs, who lived in three villages, namely Leduk, Jatiarjo and Dayurejo village, Pasuruan, East Java-Indonesia. Data obtained from respondents with questionnaires and analyzed using SEM (structural equation modeling), to find out a general description of respondents' responses about community participation in the preservation of Arjuna mount forest. The results showed that (1) human resources, economic, social, local institutional factors, facilities and infrastructure had a very significant effect on forest revegetation; (2) geography and land variables have a significant effect on forest revegetation; (3) forest revegetation variables have a significant effect on the preservation of springs; and (4) The development of human resources, economic, local institutional, facilities and infrastructure in the community around the forest was a relevant model for the success of forest revegetation and the preservation of springs in Arjuna mount.

Studies of Phytophthora impacts in forests generally focus on individual species without recogni-tion that Phytophthora occur in multispecies communities. We investigated community structure of Phytophthora species in the rhizosphere of Agathis australis (kauri) in Te Wao Nui o Tiriwa / Waitākere Ranges, New Zealand, in the context of kauri dieback disease expression. Soil sampling and tree health monitoring was conducted on 767 randomly selected mature kauri trees. Phy-tophthora species were detected using both soil baiting and DNA metabarcoding of eDNA. Four species were detected with soil baiting (P. agathidicida, P. cinnamomi, P. multivora, and P. pseudo-cryptogea) and an additional three species with metabarcoding (P. kernoviae, P. cactorum/P. aleatoria and an unknown clade 7 species). Phytophthora cinnamomi was the most abundant species and was distributed throughout the forest. Both P. multivora and P. agathidicida were limited to forest edges, suggesting more recent arrivals. P. agathidicida presence was strongly correlated with declining canopy health, confirming its role as the main river of kauri dieback. The limited distribution of P. agathidicida and infrequent detections (11.0% samples) suggests that that this species is spreading as an introduced invasive pathogen and provide hope that with strategic management uninfected areas of the forest can be protected. The frequent detections of P.cinnamomi and P. multivora, from symptomatic trees in the absence of P. agathidicida suggest more research is needed to understand their roles in kauri forest health.

In recent years, the integration of sensors in agriculture has emerged as a pivotal catalyst for transforming conventional farming practices into precision agriculture. Sensor application is impacting the everyday objects that enhance human life. In this special issue, the main objective was to address recent advantages of sensor application in agriculture covering a wide range of topics in this field. This provides overview of diverse application of sensors in agriculture, highlighting their role in enhancing productivity, sustainability, and resource management. sensors are instrumental in monitoring various agriculture parameter such as soil condition, weather, crop health and livestock wellbeing. By collecting real-time data and providing valuable insight, sensors enable precision farming, optimize resource allocation, and minimize environmental impacts. we have to explore the key sensor types used in agriculture, their applications, benefits, and future prospects, emphasizing their crucial roles in the modernization of agriculture and global quest for food security and crop production.

To provide the population with food, it is very important to re-cultivate "abandoned lands" that have been retired from agricultural use. Conversion of arable land into agricultural abandoned lands in the south of Kazakhstan is, first of all, was primarily associated with salinity. For the purposes of sustainable development, there is a need to develop proposals for re-mastering by studying their current state, reviewing world studies on the reclamation of salt-affected soils. Therefore, this study was devoted to the study of the current environmental and reclamation conditions of the abandoned lands of Otyrar district in the south Kazakhstan, as well as discussion and recommendations on their development. In the course of the research, the historical method is used in the study of the emergence and formation of abandoned lands, the method of geographical analysis during the territorial analysis of the research object, the statistical and comparison methods in showing the complexity and intensity of the problems, the cartographic, geo-informational and field research (reconnaissance) methods in the study of the condition of the abandoned lands in the research object and methods of grouping and analysis were used in the work with scientific data on the topic of research. By deciphering space images with the help of geoinformation technologies, it was revealed that the area of abandoned lands in the research object is 13688.9 ha, including the area of non-saline soils – 83.9 ha, weakly saline soils – 984.4 ha, medium saline soils – 2398.3 ha, highly saline soils – 10222,1 ha. A review of the methods and technologies proposed by scientists for the development and melioration of salt-affected lands was made. Taking into account the ecological and reclamation state of the object of research along with the material and technical capabilities of farms, 2 methods of developing abandoned lands (organic and agro-innovative) in the research object are proposed and the need for their use in case of soil salinization has been scientifically justified.

In response to increasing urbanization and the need for infrastructure resilient to natural hazards, this study introduces an AI-driven predictive model designed to assess the risk of soil liquefaction. Utilizing advanced ensemble machine learning techniques, the model integrates geotechnical and geographical data to accurately predict the potential for soil liquefaction in urban areas, with a specific focus on Yokohama, Japan. This methodology leverages comprehensive datasets from geological surveys and seismic activity to enhance urban planning and infrastructure development in smart cities. The primary outputs include detailed soil liquefaction risk maps that are essential for effective urban risk management. These maps support urban planners and engineers in making informed decisions, prioritizing safety, and promoting sustainability. The model employs a robust combination of neural networks and gradient boosting decision trees to analyze and predict data points, assessing soil susceptibility to liquefaction during seismic events. Notably, the model achieves high accuracy in predicting soil classifications and N-values, which are critical for evaluating soil liquefaction risk. Validation against an extensive dataset from geotechnical surveys confirms the model’s practical effectiveness. Moreover, the results highlight the transformative potential of AI in enhancing geotechnical risk assessments and improving the resilience of urban areas against natural hazards.

The study presented here, through the use of GIS and physical variables, generate a new meth-odology for the calculation of erosivity (Factor R) for the qualitative evaluation of soil loss po-tential (laminar erosion) in the Northwest margin of the municipality of São Francisco, mostly located in the Pardo-MG river basin. The results obtained were statistically correct when com-pared to the traditional R calculation and the laminar erosion methodology. It is worth noting that the new proposal presented here indicated sites of qualitative loss of soil in the same places where there are closed-loop satellite signals of degraded Cerrado, something not identified in the traditional model. As conclusion it can be said that the obtained results were extremely sat-isfactory, being advised more studies to verify this new method proposed here in more places beyond the study area elaborated here

Soil texture is key information in agriculture for improving soil knowledge and crop performance, so the accurate mapping of this crucial feature is imperative for rationally planning cultivations and for targeting interventions. We studied the relationship between radioelements and soil texture in the Mezzano Lowland (Italy), a 189 km² agricultural plain investigated through a dedicated airborne gamma-ray spectroscopy survey. The K and Th abundances were used to retrieve the clay and sand content by means of a multi-approach method. Linear (simple and multiple) and non-linear (machine learning algorithms with deep neural networks) predictive models were trained and tested adopting a 1:50,000 scale soil texture map. The comparison of these approaches highlighted that the non-linear model introduces significant improvements in the prediction of soil texture fractions. The predicted maps of the clay and of the sand content were compared with the regional soil maps. Although the macro-structures were equally present, the airborne gamma-ray data permits us shedding light on finer features. Map areas with higher clay content were coincident with paleo-channels crossing the Mezzano Lowland in Etruscan and Roman periods, confirmed by the hydrographic setting of historical maps and by the geo-morphological features of the study area.

Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (Cc) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, Cc was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate Cc such as Oedometer test. This study presents a novel prediction model for Cc of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of developed GEP-based model was evaluated through coefficient of determination (R2), root mean squared error (RMSE), and mean average error (MAE). High R2 and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R2, RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining Cc of fine-grained soils.

Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (C_c) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, C_c was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e₀). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate C_c such as oedometer test. This study presents a novel prediction model for C_c of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate C_c based on LL, PL, and e₀. The performance of developed GEP-based model was evaluated through coefficient of determination (R²), root mean squared error (RMSE), and mean average error (MAE). High R² and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R², RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining C_c of fine-grained soils.Keywords: evolutionary model, gene-expression programming (GEP), prediction, soil compression index, estimation, soil engineering, soil informatics, civil engineering, machine learning, data science, big data, soft computing, deep learning, forecasting, subject classification codes, construction informatics, computational intelligence (CI), artificial intelligence (AI), estimation

Biochar is a carbon-rich material that recently received attention due to its increasing agronomical potential. The agricultural utilization of biochar relates to its potential to act in the soil as a soil conditioner, nevertheless, the complex information on the direct dependence of biochar physical properties (texture, particle size) and corresponding leaching and availability of organic molecules (e.g. the polycyclic and heterocyclic organic compounds) and inorganic mineral salts (based on micro– and macroelements) are still not consistent. These aspects were addressed in our study. A multi-elemental analysis (ICP-OES) was used to access the information on the aqueous-extractable and total contents of inorganic macro- and macroelements in used commercial biochar samples (purchased from Sonnenerde GmbH, Novoterrra Sonnenerde GmbH, Biouhel.cz, s.r.o.). Potassium (K) was identified as the major macroelement, responsible for alkaline pH and the conductivity of biochar extract. Our results indicate an indirect relation between the size fraction of biochar and the content of extractable K, sodium (Na), and calcium (Ca) and a direct relation with the contents of phosphorus (P) and magnesium (Mg). As the total determined amounts of inorganic elements were significantly higher compared to aqueous-extractable amounts, we believe it could indicate the potential for repeated release to the surrounding soil. These findings were also confirmed by the analysis of biochar samples obtained after the cultivation (duration of 2 years) in selected representative soil samples from the Czech Republic (Cambisol, Fluvisol, Regosol, Chernozem). Moreover, gas chromatography (GC) coupled with mass spectroscopy (MS) was further used to reveal the concentrations of available polycyclic aromatic and heterocyclic compounds in biochar samples. The detected concentration of these types of organic compounds were far below the certified limits. We believe our in-depth analysis could provide the necessary insight into the description of biochar mineral content, its connection to biochar texture, corresponding physicochemical properties and potential availability to leach nutrients to the soil. These findings could help in further assessment of the potential applications of biochar in modern agriculture as a soil conditioner.

Sustainable food systems imply the re-use of biowastes and water. In this paper we characterize 30 top soil improvers of anthropogenic, animal and green waste origin, and 11 irrigation waters from rivers, channels, and civil wastewater treatment plants (cWWTPs) for the presence of antimicrobials. Liquid chromatography coupled to hybrid High Resolution Mass Spectrometry (LC-HRMS/MS) was applied to determine 50 different drugs belonging to the classes of sulfonamides (11), tetracyclines (7), fluoroquinolones (10), macrolides (12), amphenicols (3), pleuromutilins (2), diaminopyrimidines (1), rifamycines (1) and lincosamides (1). Biowastes from cWWTPs and animal manure, slurry and litter revealed the highest loads for sulfonamides, tetracyclines, fluoroquinolones, and macrolides (80, 470, 885, and 4,487 ng/g wet weight, respectively) with nor- and cipro-floxacin as marker of anthropogenic sources. In composts and digestates antimicrobials were almost below the determination limits. Re-used waters for irrigation in open field lettuce production resulted contaminated in the range of 12-221 ng/L for sulfonamides, tetracyclines, and fluoroquinolones, against very few detects in channels and surface waters. Antimicrobials Hazard Index (HI) based on Predicted No Effect Concentration for Antimicrobial Resistance (PNEC_AMR) was largely >100 in contaminated top soil improvers from urban and animal sources. Accounting for worst-case inputs from top soil improvers and irrigation water and dilution factors of amended soil, fluoroquinolones only showed a HI around 1 in open fields for lettuce production. The origin of top soil improvers plays a pivotal role for safe and sustainable leafy vegetable productions, to abate the risk of AMR onset in food-borne diseases, as well the transfer of AMR elements to human gut flora.

Organic farming has positive, impact on environment, soil health, and healthy food quality. Worldwide demand for organic foods is increasing by leaps and bounds in recent years. The present investigation was undertaken during 2014 to 2018 to evaluate the effect of cowpea (Vigna unguiculata) co-culture with maize (Zea mays L.) on productivity enhancement over prevailing maize-fallow system, and to assess the feasibility of inclusion of short duration winter crops after maize with appropriate residue management practices on productivity and soil health. The experiment comprised of six cropping systems in main plot and three soil moisture conservation (SMC) measures options in sub plot. Results indicated that the inclusion of second crop in place of fallow and cowpea co-culture with maize increased average maize grain yield by 6.2 to 23.5% as compared to that of maize-fallow (MF). Use of maize stover mulch (MSM) + weed biomass mulch (WBM) increases maize grain yield by 19.1 and 6.5% over those of MSM and no mulch (NM), respectively. Various soil moisture conservation (SMC) measures had significant (p=0.05) effect on crop yields and water productivity. Double cropping system had significantly (p=0.05) higher amount of soil available NPK, soil organic carbon (SOC), microbial biomass carbon (MBC) and dehydrogenase activity (DHA) at 0-15 cm and at 15-30 cm depth than those under MF. The SWC measures of MSM+WBM had significantly higher available N, SOC, and MBC by 5.5, 4.8 and 8.1% than those under NM, respectively. Correspondingly, soils under MSM and MSM+WBM had 2.24 and 2.99% lower bulk density (ρb) in 0-15 cm and 2.21 and 2.94% lower ρb in 15-30 cm than that of NM. The energy use efficiency (EUE) was significantly higher under MCV (7.90%) over rest of the cropping sequences. MSM+WBM and MSM recorded 25.1 and 16.6% higher net energy over NM, respectively. The net return (INR 159.99×103/ha) and B:C ratio (2.86) were significantly higher with MCV system followed by MCR cropping sequence. MSM+WBM had significantly higher net return (INR 109.44×103/h), B:C ratio (2.46) over those under MSM (INR 97.6×103/h) and NM (INR 78.61×103/h). Overall the cowpea co-culture with maize and inclusion of short cycle winter crops along with MSM+WBM in maize-based cropping systems was found productive in terms of crop and water, profitable, energy efficient and sustained the soil health.

Initial water content significantly affects the efficiency of soil stabilization. In this study, the effects of initial water content on the compressibility, strength, microstructure and composition of a lean clay soil stabilized by compound calcium-based stabilizer were investigated by static compaction test, unconfined compression test, optical microscope observations, environment scanning electron microscopy, energy dispersive X-ray spectroscopy and X-ray diffraction spectroscopy. The results indicate that as the initial water content increases in the range studied, both the compaction energy and the maximum compaction force decrease linearly and there are less soil aggregates or agglomerations, and smaller proportion of large pores in the compacted mixture structure. In addition, for specimens cured with or without external water supply and under different compaction degrees, the variation law of the unconfined compressive strength with initial water content is different and the highest strength value is obtained at various initial water contents. With the increase of initial water content, the percentage of oxygen element tends to increase in the reaction products of the calcium-based stabilizer, whereas the crystalline mineral of the soil did not change obviously.

The global biodiversity crisis has emphasized the unique contribution of functional diversity to ecosystem function, stability, and resilience. In this regard, the increasing availability of remotely sensed data together with the development of new sensors and approaches has the potential to improve our ability to quantify and monitor ecosystem traits and functions at unprecedent spatial, temporal, and spectral scales. In particular, air- and spaceborne hyperspectral data are making possible the measurement of plant-level functional traits to investigate ecosystem function and functional diversity in novel ways. In this review we pose that these developments together with similar ones focusing on soils and lithologies can help us understand relationships between functional diversity, ecosystem processes, and landsliding, and more broadly the disturbance dynamics of mountainscapes. Acknowledging the challenges associated with mountainscapes, this review 1) synthesizes broad established methods to retrieve functional traits from remotely sensed data, 2) summarizes approaches derived from remotely sensed data to characterize functional diversity and its relationships with ecosystem functioning, and 3) elaborates on how these methods and approaches can provide a needed holistic view of landslides. This holistic view recognizes the critical role that interactions between ecosystem and geomorphic processes play in the dynamics of mountainscapes mediated by landslides and the contribution of ecosystem diversity and processes to landslide susceptibility and recovery. In this “ecosystem-centered” view of landslides it might be necessary to scale from individual landslides and sites to entire landslide populations, assemblages, and landscapes.

The combination of organic matter, iron oxide, and clay minerals is of great significance for the adsorption of copper ions (Cu). The purpose of this study is to explore the characteristics of Cu adsorption and laws governing Cu complexation to organic–inorganic, organic–clay mineral, and iron-oxide–clay mineral complexes in the sediments in the estuary of plateau fault and sinking lake——Dianchi Lake. In this study, Cu adsorption tests were performed on the three complexes, in order to study the kinetic behavior of adsorption, Langmuir and Freundlich isotherm models were used. The samples before and after adsorption were characterized via scanning electron microscope (SEM), Fourier infrared spectroscopy (FTIR), and X-ray diffraction (XRD). Our results show that, the Freundlich isotherm models model was able to describe adsorbent behavior in comparison to the Langmuir models. During the Cu adsorption process, the iron-oxide–clay mineral complex is able to adsorb Cu, via coordination exchange, through the –OH contained therein. Organic-matter–clay mineral complexes bonded to the surfaces of clay minerals by replacing the hydroxyl groups with functional groups (carboxyl groups or phenolic hydroxyl groups) contained in the organic matter. Organic–inorganic composites then adsorbed Cu through the coordination exchange of –OH in the polar functional groups of alcohols, phenols, and carboxylic acids. The adsorption capacity of Cu in these three sediment complexes was observed to have the following order: organic–inorganic complex &gt; organic-matter–clay mineral complex &gt; iron-oxide–clay mineral complex. The semi-quantitative analysis results of Fourier Infrared Spectroscopy show that the organic matter (changes in the peak area of functional groups such as carboxyl groups) in the organic-inorganic composite material has an important effect on the amount of copper ions adsorbed by clay minerals.

During recent archeological excavations in the Roman city of Carnuntum three spots with residues of streets, houses and side buildings from the time span between 1st and 4th century have been investigated. 40 soil samples taken from different layers gave the possibility to investigate the local soil bacterial communities by Next Generation Sequencing (NGS) of 16S rRNA. The background of this study was to check similarities and differences in the composition of microbial communities and to check of special types of proved bacteria or groups of them could be related to the archaeological findings. In result, the 16S rRNA data shows similarities between some samples, but also important differences in the compositions of soil bacterial communities of single samples. On the one hand, some samples are distinguished by the dominance of single major components from other samples. On the other hand, specific differences between soil samples are reflected by the patterns of mediate and less abundant bacterial groups. This supports the hypothesis, that differences in soil bacteria composition are strongly influenced by differences of the deposed soil material and, therefore, dependent on ancient human activities which created the deposits and local ancient environmental conditions many hundred years ago. This “echo” of ancient human impact on local soil bacteria composition is reflected, for example, by manure- and oil-related OTUs originating from two different street surfaces as well as by contaminated and mixed soil fills and ancient hearths and fire places. The findings support the concept of “ecological memory of soil” meaning that early impacts impress traces in the composition of soil bacterial communities which are detectable up to the present.

In the last decade Electromagnetic Induction (EMI) measurements have been increasingly used for investigating the soil salinization caused by the use of brackish or saline water as irrigation source. EMI measurements proved to be a powerful tool for providing spatial information of the investigated soil because of the strict correlation between the output geophysical parameter, i.e. the electrical conductivity, to soil moisture and salinity. In addition, their non-invasive nature and their capability to collect a high number of data over broad areas and in relatively short time, makes these measurements attractive for monitoring flow and transport dynamics, otherwise undetectable with conventional measurements. In an experimental field, EMI measurements were collected during the growth season of the tomato, irrigated with three different irrigation strategies. The data were collected over three months in a time-lapse mode in order to visualize changes in electrical conductivity to be associated with soil salinity. A rigorous time-lapse inversion procedure has been set for modeling the soil salinization induced by brackish irrigation water.

In northern Canada where permafrost is prevalent, a shortage of accessible, affordable, and high-quality housing has been ongoing for decades. Design of foundations in permafrost presents unique engineering challenges due to permafrost soil mechanics and climate change. There is no specific design code for pile or shallow foundations in northern Canada. Consequently, the design process heavily relies on the expertise and experience of Arctic geotechnical engineers who have specialized knowledge in this field. Therefore, a comprehensive review of the practice or literature in foundation design in the Arctic would be necessary. The main objective of this review paper is to provide an overview of the common foundations in permafrost and the techniques adopted to counteract the effects of frozen soils. This study conducted a literature review of state-of-the-art practices on deep and shallow foundations used in northern Canada, including common steel pipe piles and thermosyphons. The review summarized the current principles, including adfreeze strength, creep settlement, and frost heave, used in the foundation design in permafrost. Then this paper summarized the findings from interviewing several engineers or professionals who have experiences in the design of foundations in permafrost. The interviews revealed the technical and non-technical aspects of foundation design practice in northern Canada. Lastly, in order to demonstrate the design methods, the paper presented two examples where screw piles and steel pipe piles were designed to support a residential building in northern Canada, according to the state-of-practice criteria for adfreeze strength, long term settlement and frost heave.

The Southwest Alpine Canyon Area of China was one of the regions in the world that suffered from severe soil erosion. The focus of this research was to understand the response of land use change on soil loss by water erosion in alpine canyon on Southeast Tibet, we analyzed the characteristics of temporal and spatial dynamics of land use in the Niyang River Basin from 1990 to 2020 using GIS and statistical methods, assessed the spatial and temporal change characteristics of soil erosion in the Niyang River Basin using RUSLE model. For this purpose, the meteorological, soil, topographic, remote sensing data were collected. The results showed the area of cropland and construction land in the Niyang River Basin increased to 2.4 and 6.1 times the original from 1990 to 2020, respectively, the area of forest and water continued to decrease. Grassland, unused land and forest have always been the dominant land types in the Niyang River Basin. Tolerable soil erosion was distributed in the flat areas of the valley terraces in the main stream and its tributaries, slight soil erosion was mainly distributed in the flat areas at high altitudes, and moderate, severe, very severe and destructive soil erosion was sporadically distributed in the Niyang River Basin. The areas of tolerable and slight soil erosion exceeded 82% of the total area, The area of severe, very severe and destructive soil erosion was less than 6%, but the total soil loss amount of severe, very severe and destructive erosion was more than 27%. From 1990 to 2020, the soil erosion area decreased from 9219.34 km2 to 8,090.84 km2 in Niyang River Basin, more than 85% of the soil erosion class has not changed, and the change of total soil loss amount also shows a significant reduction trend as a whole. 99% of the total soil loss amount in the Niyang River basin is generated by grassland, forest and unused land. The conversion of various land use types to cropland will cause the enhancement in soil erosion grade. We recommend that the focus of soil erosion control in the Niyang River Basin is to protect grassland, forest and carry out scientific farming.

Olive orchards cover over 10 million hectares worldwide, with production techniques undergoing significant changes in the past three decades. The traditional rainfed approach, involving minimal inputs, has given way to irrigated super-intensive systems with higher planting density, in-creased productivity, greater use of fertilizers and phytopharmaceuticals, and total mechaniza-tion. Its impact on soil chemical properties remains a topic of great debate and no definitive consensus has been reached. Our main objective was to examine the different effects of traditional olive orchards and super-intensive ones on soil chemistry over a decade. We collected and ana-lyzed 1500 soil samples from an irrigation perimeter in southern Portugal in 2003 and 2013. Our findings indicate that compared to traditional olive orchards, super-intensive ones show, in a decade, a significant decrease in soil organic matter (less 22.8% - p<0.001) and an increase in so-dization (more 33.8% of Ext Na - p<0.001) highlighting the importance of monitoring this factor for soil fertility. In comparison to other irrigated crops in the region, super-intensive olive orchards promote a significant soil acidification (from 7.12 to 6.58), whereas the pH values of the other crops increase significantly (more 3.3%, 13.5%, 3.0% in corn, tomato and cereals, respectively). Mainly because organic matter levels decrease, soil acidification and soil sodization we can un-derline that hedgerow olive orchards can affect soil characteristics negatively when compared with traditional ones.

With the development of bioinspired green solutions for sustainable construction over the past two decades, bio-cementation, which exploits the naturally occurring phenomenon of calcium carbonate precipitation in different environments, has drawn a lot of attention in both building construction and soil stabilization. Various types of microorganisms, along with specific enzymes derived from these microorganisms, have been utilized to harness the benefits of bio-cementation. Different application methods for incorporating this mechanism into the production process of the construction material, as well as a variety of experimental techniques for characterizing the outcomes of bio-cementation, have been developed and tested. Despite the success of bio-cementation as a sustainable method to construction has been demonstrated in a significant body of literature at the laboratory scale, the expansion of this strategy to construction sites and field application remains a pending subject. The issue may be attributed to two primary challenges. Firstly, the complexity of the bio-cementation phenomenon is influenced by a variety of factors. Secondly, the extensive body of literature examines various types of microorganisms under different conditions, leading to a wide range of outcomes. Hence, this study aims to examine the recent advancements in utilizing the most commonly employed microorganism, Sporosarcina Pasteurii, to emphasize the significance of influential factors identified in the literature, discuss the findings that have been brought to light, and outline future research directions toward scaling up the process.

Converting biowaste into biochar and subsequently putting it into the soil are considered to be an effective approach to sequestrate carbon (C). However, biochar will inevitably undergo the aging process in soil, which influences its stability, and ultimately threatens its carbon sequestration ability. This study selected CaCl2 as exogenous additive of sewage sludge and bone dreg to quantify both surface C and bulk C stability in Ca-rich biochars under three aging processes (dry-wet aging, freeze-thaw aging, and natural aging in farmland soil), and revealed the influence mechanisms of exogenous Ca on biochar stability. Results showed that after dry-wet aging (25 rounds), freeze-thaw aging (25 rounds), and natural aging in different farmland soils (5 months), oxidized surface C in Ca-rich biochar decreased by 10~23%, 28~41%, and 0~74%, respectively, compared to that in pristine biochar, while oxidized bulk C decreased by 6~10%, 0~1%, and 0~35%, respectively. Under three aging processes, the surface C and bulk C stability in Ca-rich biochar were superior to that in corresponding pristine biochar, which was attributed to the “protective effect” of Ca-containing crystals on biochar surface, including CaO, Ca5(PO4)3Cl, Ca5(PO4)3(OH), Ca8H2(PO4)6·H2O and Ca10(PO4)6(OH)2. These Ca-containing crystals could block the connection between biochar-C and the external oxidizing environment, intervening the oxidation of C-C/C=C in biochar, but also prevented aging from damaging C structure of biochar, reducing the generation of fragmented structure. By comprehensively assessing surface C and bulk C stability under three aging processes, final C sequestration in Ca-rich biochar increased to 27~80%, compared to that in pristine biochar (23~74%). Therefore, Ca-rich biochar is more dominant than pristine biochar, considering C sequestration potential during long-term aging in soil.

In order to study the surface settlement law of subway tunnel engineering site overlying soft soil layer under the action of earthquake, ABAQUS finite element analysis software was used to study the surface settlement law of subway tunnel construction site overlying soft soil layer under the action of three different seismic waves, including El-Centro wave, Kobe wave, and artificial seismic wave with exceedance probability of 10%, based on the Dalian Metro Line 5 with overlying soft soil layer as the engineering background, and the law was analyzed theoretically. Then, the surface settlement law of tunnel top was studied based on El-Centro wave and soft soil parameters, and the sensitivity analysis of soft soil parameters on surface settlement of tunnel top was carried out by orthogonal test method. The results show that under earthquake action, the settlement of the strata within a certain range of the tunnel roof is significantly greater than that of the surrounding strata, forming a settlement trough with a width of about 8 to 20 meters. The width of the settlement trough under the El-Centro wave is the largest, about 19.6 meters, surpassing the artificially synthesized seismic waves with a probability of 10%, which is about 15.6 meters, while the width of the settlement trough under the Kobe wave is the smallest, about 8.5 meters. The ground surface within a range of about 20 meters above the tunnel roof is most strongly affected by the seismic waves and the special lithology of the overlying soft soil layer, and the settlement is the largest. The settlement law of the settlement trough in the overlying strata of the tunnel conforms to the Peck formula. Increasing the elastic modulus of the silty soil can reduce the settlement of the ground surface above the tunnel roof, increasing the Poisson's ratio of the silty soil will increase the settlement of the ground surface above the tunnel roof, increasing the cohesive force of the silty soil to 20 kPa will basically stabilize the settlement of the ground surface above the tunnel roof, and increasing the internal friction angle of the silty soil will basically not change the settlement of the ground surface above the tunnel roof. The sensitivity of the soft soil parameters to the settlement of the ground surface above the tunnel roof is in the order of Poisson's ratio, elastic modulus, cohesive force, and internal friction angle.

Alum sludge is a byproduct of water treatment plants and its use as a soil stabilizer has gained increasing attention due to its economic and environmental benefits. Its application has been shown to improve the strength and stability of soil, making it suitable for various engineering applications. However, to go beyond just measuring the effects of alum sludge as a soil stabilizer, this paper explores the use of artificial intelligence (AI) methods to predict the California bearing ratio (CBR) of soils stabilized with alum sludge. Three AI methods, including two black box methods (artificial neural network and support vector machines) and one grey box method (genetic programming), were used to predict CBR based on a database with nine input parameters. The results showed that all three AI models were able to predict CBR with good accuracy, with coefficient of determination (R2) values ranging from 0.94 to 0.99 and mean absolute error (MAE) values ranging from 0.30 to 0.51. In a novel approach, the genetic programming method was used to produce an equation to estimate CBR, which included seven inputs and accurately predicted CBR. The analysis of sensitivity and importance of parameters showed that the number of hammer blows for compaction was the most important parameter, while the parameters for maximum dry density of soil and mixture were the least important. This study suggests that AI methods can effectively predict the performance of alum sludge as a soil stabilizer, and the proposed equation using genetic programming can be a useful tool for predicting CBR.

Potato (Solanumtuberosum L.) is the world's largest non-cereal crop, occupying a position critical for global food security, the fourth largest crop food in China. Due to its location and weather conditions, Yunnan province is the fourth largest potato producer in China with twice yearly production system. However, compared to high number of consumers, potato yield is still low to satisfy the population need. In this study, we have analyzed impacts of three cultivation factors: (1) three cultivation models (T1, T2, and T3), (2) planting density (T1, T2, and T3), (3) soil covering (T1, T2, T3, T4) on plant growth, and yield production. The soil moisture content, temperature, roots growth, stems development, leaf area index, and number-weight of tubers per plant and per plot (g) have significantly increased with commercial yield average of 3tones/mu. This suggests that the combination of these three factors would be a great potato extension model in winter climate zones similar to Yunnan Province. This will contributes in satisfaction of the world population potato consumption need.

Land degradation a serious and nationwide environmental concern in Ethiopia. The problem is its iterative relationship between land degradation, climate change, and agriculture, exacerbating one another via negative and positive feedback loops. Due to the need for an efficient response to land degradation in the country, different sustainable land management practices have been implemented since the late 1980s. The objective of this study was to analyze land degradation neutrality status using remote sensing data in the study area. We have studied the land degradation neutrality conditions of the North Wello Zone by using indicators data, namely land cover change, land productivity dynamics, and soil organic carbon stock. The result shows that the settlement areas consistently expanded at the fifth speed (2010-2018) from 1995 to 2010. Between 1995 and 2010, forestland declined by 18 percent, while an increasing trend of 26.8 percent from 2010 to 2018. The assessment results also indicate that 52.8 percent of the total area is stable and characterized by less stressed land productivity. The soil organic carbon is comparatively abundant in the high and midland vegetation areas but very thin in lowland areas. Most of the highland and midland parts of the study areas are in the conditions of stable and increasing land productivity with high biomass and soil organic content. However, most lowland areas showed a decline in land productivity conditions.

Soil hydraulic properties are often derived indirectly, i.e. computed from easily available soil properties with pedotransfer functions (PTFs), when those are needed for catchment, regional or continental scale applications. When predicted soil hydraulic parameters are used for the modelling of the state and flux of water in soils, uncertainty of the computed values can provide more detailed information when drawing conclusions. The aim of this study was to update the previously published European PTFs (Tóth et al., 2015, euptf v1.4.0) by providing prediction uncertainty calculation built into the transfer functions. The new set of algorithms was derived for point predictions of soil water content at saturation (0 cm matric potential head), field capacity (both -100 and -330 cm matric potential head), wilting point (-15.000 cm matric potential head), plant available water, and saturated hydraulic conductivity, as well as the Mualem-van Genuchten model parameters of the moisture retention and hydraulic conductivity curve. The minimum set of input properties for the prediction is soil depth and sand, silt and clay content. The effect of including additional information like soil organic carbon content, bulk density, calcium carbonate content, pH and cation exchange capacity were extensively analysed. The PTFs were derived adopting the random forest method. The advantage of the new PTFs is that they i) provide information about prediction uncertainty, ii) are significantly more accurate than the euptfv1, iii) can be applied for more predictor variable combinations than the euptfv1, 32 instead of 5, and iv) are now also derived for the prediction of water content at -100 cm matric potential head and plant available water content.

Faecal sludge (FS) contains a significant amount of plant nutrients. After drying and composting, FS has been used as soil ameliorant in several countries. Use of FS-based compost on lettuce may meet reservations due to possible microbiological contamination. The objectives of this research are: (l) to determine the fertilizer value of different formulations of sawdust and faecal sludge compost (SDFS) pellets, (2) to compare the effect of these SDFS formulations with poultry manure, commercial compost, mineral fertilizer and non-fertilization on lettuce cultivation. The SDFS products were made by enriching and pelletized with ammonium sulphate, mineral-NPK or ammonium sulphate + muriate of potash + triple super phosphate. Lettuce was cultivated in a greenhouse and in an open field. The result showed that the fresh weight obtained from all SDFS pellets with/without enrichments were higher than those obtained from commercial compost, poultry manure, mineral fertilizer or no fertilizer. Cultivation in the open field gave higher yields than those in the greenhouse. No helminth eggs were detected in composts or lettuces. Some faecal coliforms were detected in lettuces despite fertilization treatments. A properly treated fecal sludge-based fertilizer can be a sustainable solution for lettuce production which helps urban and peri-urban agriculture.

Stable isotope concentrations in the soil, rain and ground water have been used to trace the water extraction zones of plants in different environments. The need to identify the plant water use by plants in afforestation programs to control desertification increases the importance of sap water partitioning of plants in sand dune areas. However, the introduction of new plant covers exerts pressure on the water resources and can affect the local soil water conditions. In this study, we analyzed the isotope concentrations in rain, soil, sap, and ground water after the summer of 2010. Two experimental plots established in the Hailiutu catchment (Shaanxi province, northwest China) were selected to gather the water samples between September and October 2010. One plot is dominated by Salix bushes (Salix psammophila C. Wang \& Chang Y. Yang) and the other by the tree species Willow (Salix matsudana Koidz.). The total precipitation at the experimental site was 401 mm/yr during 2010, while 88.7 mm was collected in total for the period September to October. Willow trees transpired 12.82 kg/d being almost three times larger than Salix shrubs (4.57 kg/d). Despite the transpiration rates of both plant species and the few rain events in the region, the soil water beneath the plant covers is not depleted. Stable isotope signature of soil water beneath both covers shows the fractionation front in Salix at 20 cm depth and at Willow at 40 cm depth. However, soil water signature is closer to the groundwater than the collected rain water.

Water, soil/sediment, and 36 fish samples were collected at three major sites along Flint Creek in 2015-2016 and analyzed for total organic carbon (TOC), dissolved organic carbon (DOC), total mercury (tHg), and other water quality indicators. This study was a follow-up to a 2012 study that revealed elevated tHg levels in fish, resulting in a public health advisory. This study revealed tHg concentrations in water below the detection limit (0.0002 ppm); while, tHG in soil/sediment ranged from < 0.0133 ppm to 0.0682 ppm dry weight. No temporal trends existed, but tHG tended to increase with TOC. Mercury levels in sediment were below the threshold effects level suggested as a preliminary screening level by the National Oceanic and Atmospheric Administration (acute = 1.4, chronic = 0.77 ppb). In summary, tHg levels were low; posing little risk to drinking water. Soil/sediment levels were generally higher and could pose risk to aquatic species.

Soil above a platinum-group element (PGE)-bearing horizon within the Freetown Layered Intrusion, Sierra Leone contains anomalous concentrations of n-alkanes (CnH2n+2) in the range C14 to C22 not readily attributable to an algal or lacustrine origin. Longer chain n-alkanes (C23 to C31) in the soil were derived from the breakdown of leaf litter beneath the closed canopy humid tropical forest. Spontaneous breakdown of the longer chain n-alkanes to form C14-22 n-alkanes without biogenic or abiogenic catalysts is unlikely as the n-alkanes are stable. In the Freetown soil, the catalytic properties of the PGE (Pt in particular) may lower the temperature at which oxidation of the longer chain n-alkanes can occur. Reaction between these n-alkanes and Pt species such as Pt2+(H2O)2(OH)2 and Pt4+(H2O)2(OH)4 can bend and twist the alkanes, and significantly lower the Heat of Formation. Acknowledging the possibility of microbial catalysis and the difficulty of identifying a direct organic geochemical source of the lighter n-alkanes, this paper explores the theoretical potential for abiogenic Pt species catalysis as a mechanism of breakdown of the longer n-alkanes to form C14-22 alkanes. This novel mechanism could substantiate the presence of the PGE in solution predicted by soil geochemistry and illustrate processes involving the PGE. Graphical Abstract

Soil-structure interaction (SSI) could affect the seismic response of structures. Since liquid storage tanks are vital structures and must continue their operation under severe earthquakes, their seismic behavior should be studied. Accordingly, the seismic response of liquid storage tanks founded on half space soil is scrutinized under different earthquake ground motions. To better comparison, the six considered ground motions are classified based on their pulse like characteristics, into two groups, named far and near fault ground motions. To model the liquid storage tanks, the simplified mass-spring model is used and the liquid is modeled as two lumped masses known as sloshing and impulsive, and the interaction of fluid and structure is considered using two coupled springs and dashpots. The SSI effect, also, is considered using a coupled spring and dashpot. Besides, four types of soils are used to consider wide variety of soil properties. To this end, after deriving the equations of motion, the MATLAB programming is employed to obtain the time history responses. Results show that although the SSI effect leads to decrease the impulsive displacement, overturning moment and normalized base shear, the sloshing (or convective) displacement is not affected by such effects due to its long period.

The United States is plagued with the highest rates of preventable metabolic diseases it has ever seen, and while poor nutrition is increasingly recognized as a critical contributing factor, good nutrition has been shown to be a potent factor in prevention and management of these illnesses. Notably, nutrition is inextricably intertwined with farming practices and the stewardship of our environment – particularly its soil. In this proposal, we propose a method to expose medical students to basic agricultural and environmental knowledge regarding the production of food, as well as educate them in practical nutrition education within the interactive, case-based, and longitudinal preclinical curriculum at Case Western Reserve University School of Medicine (CWRU SOM). We propose a two-part approach; first: integrating relevant topics in nutrition, culinary medicine, and farming practices into the preclinical blocks through Official Learning Objectives, and second: an optional 8-week, zero credit elective for students interested in pursuing a deeper understanding of these topics. Through these interventions, we believe courses like this will support a generation of physicians able to understand health from soil to plate with a consideration for the environment in addition to exemplifying healthful lifestyles themselves. We believe these kinds of future physicians will be the most effective in treating (and ideally reversing) the chronic disease epidemic.

Loess was deposited in the Negev during the Pleistocene, but such sediments seem to be missing for the Holocene. This could be due to erosion unless structures such as ruins offered protection. We studied soils developed on archaeological hilltop ruins in the Negev and the Petra region and compared them with local soils, paleosols, geological outcrops, and current dust. The ruin soils in both regions were found to consist of similarly complex mixtures of local and remote sediment sources. They differ from sediments deposited during current dust storms. This seems due to fixation processes: average accretion rates are estimated to ~0.14 mm/a, suggesting that only ~3% of the current dust that can be trapped with dry marble dust collectors is stored in the soils. Vegetation, biocrusts, and/or clast pavements associated with vesicular layers seem to act as sediment-fixing agents. As well, climate might play a role: rain, and in particular one snowstorm in the Petra region brought a high amount of sediment that was more similar to the ruin soils. Wet deposition and snow might catalyze dust deposition and enhance fixation by fostering vegetation and crust formation. Frequent snow during the Pleistocene might be one explanation of enhanced loess deposition.

Soil erosion seriously affects vineyards. In this study, the influence of two plant covers on soil moisture and the effect of different physiographic conditions on runoff and sediment yields were evaluated in a rainfed vineyard formed by four fields (NE Spain) during 15 months. One field had spontaneous vegetation as plant cover and three fields had a cover crop of common sainfoin. The vineyards’ rows were dry and stable, whereas the inter-row areas were wet although very variable, and the corridors were wet and very stable. Soil moisture in the inter-row areas with Common sainfoin was much higher than in the rows (62% - 70%) whereas this difference was lower with spontaneous vegetation (40%). Two runoff and sediment traps (STs) were installed in two ephemeral gullies, and 26 time-integrated surveys (TIS) done. The mean and maximum runoff yields were 9.8 and 30.7 l TIS^–1 in ST2 and 13.5 and 30.2 l TIS^–1 in ST3. The mean turbidity was 333 and 19 g l^–1, and the maximum sediment yields were 41,260 and 2,778 g TIS^–1 in ST2 and ST3. Changes in the canopy covers (grapevines and plant covers) and rainfall parameters explained the runoff and sediment dynamics.

Ecosystem surveillance monitoring is critical to managing natural resources and especially so under changing environments. Despite this importance, the design and implementation of monitoring programs across large temporal and spatial scales has been hampered by the lack of appropriately standardised methods and data streams. To address this gap, we outline a surveillance monitoring method based on permanent plots and voucher samples suited to rangeland environments around the world that is repeatable, cost-effective, appropriate for large-scale comparisons and adaptable to other global biomes. The method provides comprehensive data on vegetation composition and structure along with soil attributes relevant to plant growth, delivered as a combination of modules that can be targeted for different purposes or available resources. Plots are located in a stratified design across vegetation units, landforms and climates to enhance continental and global comparisons. Changes are investigated through revisits. Vegetation is measured to inform on composition, cover and structure. Samples of vegetation and soils are collected and tracked by barcode labels and stored long-term for subsequent analysis. Technology is used to enhance the accuracy of field methods, including differential GPS r plot locations, instrument based Leaf Area Index (LAI) measures, and three dimensional photo-panoramas for advanced analysis. A key feature of the method is the use of electronic field data collection to enhance data delivery into a publicly-accessible database.Our method is pragmatic, whilst still providing consistent data, information and samples on key vegetation and soil attributes. The method is operational and has been applied at more than 704 field locations across the Australian rangelands as part of the Ecosystem Surveillance program of the Terrestrial Ecosystem Research Network (TERN). The methodology enables continental analyses, and has been tested in communities broadly representative of rangelands globally, with components being applicable to other biomes. Here we also recommend the consultative process and guiding principles that drove the development of this method as an approach for development of the method into other biomes. The consistent, standardised and objective method enables continental, and potentially global analyses than were not previously possible with disparate programs and datasets.

A plant’s health and productivity is influenced by its associated microbes. Although the common microbiome is often thought to be the most influential, significant numbers of rare or uncommon microbes (eg. specialized endosymbionts) may also play an important role in the health and productivity of certain plants in certain environments. To help identify rare/specialized bacteria and fungi in the most important angiosperm plants, we contrasted microbiomes of the shoots, roots and rhizospheres of Arabidopsis, Brachypodium, maize, wheat, sugarcane, rice, tomato, coffee, common bean, cassava, soybean, switchgrass, sunflower, Brachiaria, barley, sorghum, and pea. Plants were grown inside sealed jars on sterile sand or field soil. About 95% and 86% of fungal and bacterial diversity inside plants was uncommon, however judging by read abundance, up to half of the mycobiome consists of uncommon fungal cells, while less than 11% of bacterial endophytes are rare. Uncommon seed transmitted microbiomes consisted mostly of Proteobacteria, Firmicutes, Bacteriodetes, Ascomycetes and Basidiomycetes that most heavily colonized shoots, to a lesser extent roots and least of all rhizospheres. Soil served as a more diverse source of rare microbes than seeds, replacing or excluding the majority of the uncommon seed transmitted microbiome. With the rarest microbes, their colonization pattern could either be the result of stringent biotic filtering by most plants, or uneven/stochastic inoculum distribution in seeds or soil. Several strong plant-microbe associations were observed such as seed transmission to shoots, roots and/or rhizospheres of Sarocladium zeae (maize), Penicillium (pea and Phaseolus), and Curvularia (sugarcane), while robust bacterial colonization from cassava field soil occurred with the cyanobacteria Leptolyngbya into Arabidopsis and Panicum roots, and Streptomyces into cassava roots. Some abundant microbes such as Sakaguchia in rice shoots or Vermispora in Arabidopsis roots appeared in no other samples, suggesting they were infrequent, stochastically deposited propagules from either soil or seed (impossible to know based on the available data). Future experiments with culturing and cross inoculation of these microbes between plants may help us better understand host preferences and their role in plant productivity, perhaps leading to their use in crop microbiome engineering and enhancement of agricultural production.

Surface soil moisture (SSM) is a significant factor affecting crop growth. This paper presents a method for retrieving SSM over wheat-covered areas using synergy dual-polarization C-band Sentinel-1 synthetic aperture radar and Sentinel-2 optical data. Firstly, a modified water cloud model (WCM) was proposed to remove the influence of vegetation from the backscattering coefficient of the radar data. The vegetation fraction was then introduced in this WCM, and the vegetation water content (VWC) was calculated using multiple linear regression model. Subsequently, the support vector regression technique was used to retrieve the SSM. This approach was validated using in-situ measurements of the wheat field in Hebi, in the north of Henan Province. The key findings of this study are as follows: (1) Based on vegetation indices obtained from Sentinel-2; the proposed VWC estimation model can effectively eliminate the influence of vegetation; (2) compared with vertical transmit and horizontal receive polarization, vertical transmit and vertical receive polarization is better for detecting changes in SSM at different growth stages of wheat; and, (3) the validation results indicated that the proposed approach, based on Sentinel-1 and Sentinel-2 data, successfully retrieved SSM in the study area.

Reduced nutrient mineralization rates under minimum tillage are usually compensated by mineral fertilizer application. These cannot be applied in organic farming systems, however. We hypothesized that organic minimum tillage based on frequent cover cropping and application of dead mulch will improve soil fertility and can compensate for the potential negative effects of minimum tillage. Two long-term field experiments were set up in 2010 and 2011 comparing plough versus minimum tillage including application of transferred mulch. As second factor, the application of compost versus mineral potassium and phosphorus was compared. In 2019, soils were analyzed for soil pH, organic carbon, macro-, micronutrients, microbial biomass, microbial activity and total nematode abundance. In addition, performance of pea in the same soils was determined under greenhouse conditions. Across both experiments, macronutrients (+52%), micronutrients (+11%), microbial biomass (+51%), microbial activity (+86%), and bacterivorous nematodes (+112%) increased in minimum tillage compared with the plough-based system. In the greenhouse, pea biomass was 45% higher in the soil that had been subjected to minimum tillage compared to the plough. In conclusion, soil fertility can be improved in organic minimum tillage systems by intensive cover cropping and application of dead mulch to levels higher than in a plough-based system.

In this paper, we present the application of a special soil resistivity model, the Exponentially Graded Multilayered Soil model, to the interpretation of Borehole Resistivity Logging surveys. The model, which was recently introduced by the authors, offers an alternative to the interpretation provided by constant resistivity layer models. The model is applied to synthetic soil surveys as well as to a real survey that is not satisfactorily interpreted using conventional models.

In the recent years, with the advent of Artificial Intelligence (AI) traditional methods have seen a significant transformation in the agriculture sector, especially in soil management. Soil management involves practices that maintains and improves the physical, chemical as well as the biological properties of the soil. Soil health management is essential for both the environmental conservation and sustainable agriculture production, ensuring the soil productivity and functional aspects associated with the ecosystem. Soil health management is one of the most important aspects of agriculture and food production, hence preserving and enhancing the soil health is an essential factor for supporting agriculture. Integration of Artificial Intelligence (AI) technologies with soil health management offers the potential to enhance agricultural sustainability, productivity, adapt to the climatic changes and resource constraints. The study of AI tools that can help improve soil health management by providing more accurate and efficient monitoring, analysis and decision-making capabilities. This paper studies the potential AI technologies including machine learning, robotics, and remote sensing in enhancing soil health, raising crop yields, and lowering environmental concerns by examining previous research and case studies.

This study examines the latest innovation in wooden pile foundation models using a combination of wood and concrete materials for house construction on swampy land. The focus of the research is on developing joints and materials that enhance the load-bearing capacity and stability of building structures on swampy terrain. The methods used include field observations, laboratory experiments, and descriptive analysis. The results show that the combination of wood and concrete in foundation piles can increase structural strength, reduce deformation, and extend the service life of buildings. The new joint techniques developed in this research are effective in improving structural integrity and minimizing the negative impacts of soft soil conditions. The conclusion emphasizes the importance of material innovation and joint techniques in construction on swampy land, providing practical guidance for construction practitioners and researchers in this field.

The design is described of a readily useable technology for routine paddock-scale soil porosity estimation. The method is non-contact (proximal) and typically from sensors mounted on a small farm vehicle around 1 m above the soil surface. Challenges arise from the need to have a compact low-power rigid structure and to allow for pasture cover and surface roughness. The high-frequency regime for acoustic reflections from a porous material is a function of the porosity , the tortuosity , and the angle of incidence . There is no dependence on frequency, so measurements must be conducted at two or more angles of incidence in order to obtain two or more equations in the unknown soil properties and . A system requiring a single transmitter/receiver pair to be moved from one angle to another is not viable for rapid sampling. Therefore, the design includes at least two transmitter/reflector pairs placed at identical distances from the ground so that they would respond identically to power reflected from a perfectly reflecting surface. A single 25 kHz frequency is a compromise which allows for the frequency-dependent signal loss from a natural rough agricultural soil surface. Multiple-transmitter and multiple-microphone arrays are described which give a good signal to noise ratio while maintaining a compact system design. The resulting arrays have a diameter of 100 mm. Pulsed ultrasound is used so that the reflected sound can be separated from sound travelling directly through the air horizontally from transmitter to receiver. The average porosity estimated for soil samples in the laboratory and in the field is found to be within around 0.04 of the porosity measured independently. This level of variation is consistent with uncertainties in setting the angle of incidence, although assumptions made in modelling the interaction of ultrasound with the rough surface no doubt also contribute.

Urban gardens have important social, environmental and economic roles in big cities, contributing to their sustainability. However, food production in urban gardens may be compromised due to soil pollution that resulted from decades of industrial non-regulated environmental activity and mobile transport. In this study, 12 soils from the urban agricultural area of Rome (Italy) were analyzed for the metals Be, Ba, Pb, Co, Ni, V, Zn, Hg, Cd, As, Cu, and Cr. All but one of the soils under analysis were characterized by at least one metal concentration above the threshold limit defined by the Italian Legislative Decree 152/2006 (ILD) for agricultural soils. Multivariate analysis showed that the soils could be classified into five clusters: clusters I and II had relatively lower mean metal concentrations; clusters III, IV, and V had relatively higher mean metal concentrations with several metal concentrations above the threshold proposed by ILD. Three factors contributing to the variability of the metal’s concentration in the soils under investigation were identified: geological factor, related with metals As, Ba, Be, and V; anthropogenic factor, related with Pb and Cu; and, a mixed factor related with Co, Cr, Ni, and Zn. High metal content may limit the utilization of urban soils as urban gardens for food production.

This study aimed to simulate dynamic irrigation management zones (MZs) in two maize fields for a variable rate hose-reel fertigation machine (VRFM) with a 4-section boom control. Soil moisture content was measured from nine and four soil moisture sensors in Field 1 (8.2 ha) and Field 2 (2.5 ha), respectively in different dates during the 2022 crop season. Three- and five-MZs scenarios were simulated per irrigation and the theoretical maps were processed for implementation. The application maps fitted to the VRFM showed significant spatiotemporal variations in irrigation requirements. For instance, in Field 1, 3-MZs modelling showed that the areas requiring High (H), Medium (M), and Low (L) level irrigation on July 21 were 1.60, 4.84, and 1.85 ha, respectively, whereas the farmer applied uniform rate over the whole field. H level sub-areas ranged between 1.22 ha (July 25) and 3.25 ha (July 7), showing a coefficient of variation (CV) of 43.32% for the 3-MZs, whereas H level sub-areas for 5-MZs varied from 0.41 ha (July 2) to 1.49 ha (July 7) with a CV value of 48.84%. High levels of within-field variability can be addressed using precise and dynamic irrigation MZs fitted to the irrigation technology used.

Considering future tasks in soil health, resources management and environmental protection, farmers are challenged to develop sustainable strategies for managing soil and land resources. In this study, the long-term sustainability of two fertilization strategies, - current, with synthetic fertilizers (SYN) vs conservative, with organic sources of nitrogen (organic amendments plus green manure with a legume - CONS) - was assessed in a processing tomato/durum wheat rotation. The EPIC model was used, validated with field data then run to simulate the management for 30 years under 3 current and future climates. Yield, soil organic carbon (SOC) stock change, nitrogen use efficiency (NUE), water use efficiency (WUE) and nitrate leaching were considered as sustainability indicators. Under all the future climate scenarios, tomato yield increased in CONS, remaining almost stable in SYN. Wheat yield increased both in CONS and SYN, but the average yield in CONS was considerably lower than in SYN. NUE and nitrate leaching followed the same trend, both decreasing in CONS, while WUE was higher in CONS compared to SYN. The effect of CONS on SOC was always positive. Thus, the alternative N fertilization strategy proposed can be a favorable option for maintaining soil health and a sustainable crop production.

Soil moisture maps are essential for hydrological, agricultural and risk assessment applications. To best meet these requirements, it is essential to develop soil moisture products at high spatial resolution which is now made possible using the free Sentinel-1 (S1) SAR (Synthetic Aperture Radar) data. Some soil moisture retrieval techniques using S1 data relied on the use of a priori weather information in order to increase the precision of soil moisture estimates, which required access to a weather forecasting framework. This paper presents an improved and fully automated solution for high-resolution soil moisture mapping in bare agricultural areas. The proposed solution derives a priori weather information directly from the original Sentinel images, thus bypassing the need for a weather forecasting framework. For soil moisture estimation, the neural network technique was implemented to ensure the optimum integration of radar information. The neural networks were trained using synthetic data generated by the modified Integral Equation Model (IEM) model and validated on real data from two study sites in France and Tunisia. Main findings showed that the use of radar signal averaged over grids of a few km2 in addition to radar signal at plot scale instead of a priori weather information, provides good soil moisture estimations. The accuracy is even slightly better comparatively to the accuracy obtained using a priori weather information.

The difficulty in obtaining commercial fertilizers by smallholder farmers in sub-Saharan Africa makes it very important to optimize the use of local organic resources. In VilanKulo, Mozambique, a study was carried out on okra (Abelmoschus esculentus) over two growing seasons in a haplic Lixisol, a loamy-sand textured soil, applying both bat guano and biochar prepared by an artisanal process. Bat guano was applied at two rates (5 and 10 t ha−1), one month and just before sowing. Biochar was also used at two rates (5 and 10 t ha−1) applied close to sowing. Mixtures of biochar and guano at two rates (1 and 4 t ha−1, and 2 and 8 t ha−1, respectively) were also used along with a non-fertilized control. Field trials were arranged in a completely randomized design with three replicates. The treatments receiving high rates of guano tended to show significantly higher fruit yields (&gt; 10 t ha−1, being the two-year average) in comparison to the control, which showed the lowest average okra fruit yield (6.21 t ha−1). In guano treatments, the apparent recovery by okra of some important nutrients, such as nitrogen (N), was greater than the amount of nutrient contained in the guano itself. This result, together with many others related to tissue nutrient concentration, soil properties and residual fertilizing value in guano plots, indicates a strong mineralization of guano during the growing season, probably due to its low carbon (C)/N ratio and favourable environmental conditions for the mineralization process, such as high temperature and well-aerated soil. The result also suggests some kind of manuring effect, i.e., a fertilizing effect of guano beyond what can be explained by the nutrient supply. The use of biochar increased total organic C in the soil and cation exchange capacity (CEC) compared to the control, but did not affect variables related to plant performance, such as tissue nutrient concentration or plant growth and yield.

Regeneration is crucial for forest continuity in natural and managed stands. Analyzing intra-annual dynamics improves the understanding between growth and climate, identifying survival thresholds. The objective was to determine microclimate constraints (rainfall, air and soil temperatures) over Nothofagus antarctica regeneration growing at closed, open, and edge forests in Patagonia. We measured stand characteristics (forest structure, understory, soil properties, animal use), microclimate, and daily growth of regeneration using dendrometers (n = 6) during two growing seasons. We found significant differences in the studied variables (e.g. overstory, light, soil, understory, animal use). These changes defined microclimate across overstory gradient (e.g. soil moisture), influencing the daily growth across seasons (lag, exponential, stationary). Rainfall influenced more than temperature. Daily growth in closed forests indicating shrinkage (-0.0082 mm day-1 without rain, and -0.0008 mm day-1 with 0.0-0.2 mm day-1 rainfall), while above 0.2 mm day-1 rainfall, growth always increased. Open forests presented shrinkage during days without rain (-0.0051 mm day-1), showing positive growths according rainfall. Edge forests always presented positive daily growths. The resilience of regeneration under these changed conditions was related with overstory. Main outputs indicated that regeneration was vulnerable during non-rainy days, suggesting the needs of long-term monitoring to develop better silvicultural proposals.

This paper reports on a series of dynamic simple shear tests conducted to investigate the influence of particle shape on the damping ratio of dry sand. The tests were conducted on sand samples subjected to simple cyclic shear tests to evaluate their cyclic behavior. The particle shape was quantified using three shape parameters: roundness, sphericity, and regularity. The sand samples were subjected to twelve different scenarios with varying vertical stresses and cyclic stress ratios (CSR), in both constant and controlled stress states. Each scenario involved five cyclic tests, using the same sand that was reconstructed from its previous cyclic test. After each cyclic test, hysteresis loops were created to determine the damping ratio. The results showed that the shape of the sand particles changed during cyclic loading, becoming more rounded and spherical, which resulted in an increase in damping ratio. Moreover, the paper presents two artificial intelligence models, an artificial neural network (ANN) and a support vector machine (SVM), which were developed to predict the effect of grain shape on the damping ratio. The models were found to be effective in predicting the damping ratio based on the shape of the grain, vertical stress, CSR, and number of loading cycles. Furthermore, a parameter analysis was conducted to identify the most important shape parameter, which was found to be vertical stress and regularity, while parameter CSR was the least important. Overall, this study contributes to a better understanding of the relationship between particle shape and damping ratio, which could have practical implications for geotechnical engineering applications.

Apple replant disease (ARD) is a severe problem in orchards and tree nurseries caused by yet unknown soil biota that accumulate over replanting cycles. This study tested the contribution of nematodes to ARD, and cultivation of Tagetes as a control option. In a pot experiment, Tagetes patula or Tagetes tenuifolia were grown in ARD soil, incorporated or removed. Nematodes extracted from untreated ARD soil and washed on 20 µm-sieves induced ARD symptoms when inoculated to apple saplings growing in a sterile substrate. In contrast, nematodes from Tagetes treated ARD soil did not reduce root growth compared to uninoculated plants, irrespective of Tagetes species and incorporation. In plots of five apple tree nurseries or orchards, either Tagetes or grass was grown on ARD soil. Nematodes extracted from the grass plots and inoculated to apple saplings significantly reduced plant growth compared to nematodes from Tagetes plots for all five farms. Apple rootstocks showed overall a significantly higher increase in shoot base diameter when grown on Tagetes-treated plots compared to grass plots, while this effect differed among farms. Plant-parasitic nematodes were too low in abundance to explain plant damage. In conclusion, the free-living nematodes involved in ARD can be controlled by Tagetes.

Convective rainfall can cause dangerous flash floods within less than six hours. Thus, simple approaches are required for issuing quick warnings. The Flash Flood Guidance (FFG) approach pre-calculates rainfall levels (thresholds) potentially causing critical water levels for a specific catchment. Afterwards, only rainfall and soil moisture information is required to issue warn-ings. This study applied the principle of FFG to the Wernersbach Catchment (Germany) with excellent data coverage using the BROOK90 water budget model. The rainfall thresholds were determined for durations of 1 to 24 hours, by running BROOK90 in “inverse” mode, identifying rainfall values for each duration that led to exceedance of critical discharge (fixed value). After calibrating the model based on its runoff, we ran it in hourly mode with four precipitation types and various levels of initial soil moisture for the period 1996 – 2010. The rainfall threshold curves showed a very high probability of detection (POD) of 91% for the 40 extracted flash flood events in the study period, however, the false alarm rate (FAR) of 56% and the critical success index (CSI) of 42% should be improved in further studies. The approach proved potential as an early flood indicator for head-catchments with limited available information.

Intercropping cover crops have become mandatory in areas at risk of nitrogen leaching to groundwater. These covers include several attractive late-flowering entomophilous species. They can therefore represent crucial floral resources (pollen and nectar) for pollinating insects in early autumn. Pesticides used in previous crops, however, represent a potential risk for pollinators when they are transferred to the intercropping cover plants and their floral resources. We studied the potential transfer of clothianidin (a neonicotinoid insecticide), applied two years earlier in a beet cultivation, from soil to plants and to the floral resources of three common cover species: Phacelia tanacetifolia, Sinapis alba, and Vicia faba. Soils, entire plants, flowers, and nectar were collected from plants grew in greenhouses, and soils and pollen were collected on a treated field. Our results showed that clothianidin was still present in soils (4.5 ng g−1). The residues accumulated in plants (5-15 times higher concentrations than in soils) and were present in pollen of both Vicia faba (0.07 ng g−1) and Sinapis alba (1.7 ng g−1) and in nectar of both Sinapis alba and Phacelia tanacetifolia.

Interactions of soil moisture with plant root systems are very important for plant growth. For non-invasive determination of volumetric soil moisture in a rhizobox, a microwave system based on transmittance of electromagnetic waves in the microwave frequency range was developed using microstrip patch antennas. Vector Network Analyzers (VNAs) were used to measure the S-parameters at frequency ranges close to 5 GHz. A transmission system with microstrip patch antennas was developed. The result of this attenuation is in the frequency domain. The antennae were designed as resonant microstrip antennae. The antennae were placed on both sides of a rhizobox, which allowed non-invasive measuring soil moisture in the box. The attenuation (S21(dB)) was used to measure the effect of temperature, and different types of soil; as well as sensitivity, reproducibility and repeatability of the system. In this work we present quantitative results of soil moisture in rhizobox. The microwave technique, using microstrip patch antennas, is a reliable and accurate system, and showed very promising potential applications for rhizobox-based investigations of root performance.

If given the correct remotely sensed information, machine learning can accurately describe soil moisture conditions in a heterogeneous region at the large scale based on soil moisture readings at the small scale through rule transference across scale. This paper reviews an approach to increase soil moisture resolution over a sample region over Australia using the Soil Moisture Active Passive (SMAP) sensor and Landsat 8 only and a validation experiment using Sentinal-2 and the Advanced Microwave Scanning Radiometer (AMSR-E) over Nevada. This approach uses an inductive localized approach, replacing the need to obtain a deterministic model in favor of a learning model. This model is adaptable to heterogeneous conditions within a single scene unlike traditional polynomial fitting models and has fixed variables unlike most learning models. For the purposes of this analysis, the SMAP 36 km soil moisture product is considered fully valid and accurate. Landsat bands coinciding in collection date with a SMAP capture are down sampled to match the resolution of the SMAP product. A series of indices describing the Soil-Vegetation-Atmosphere Triangle (SVAT) relationship are then produced, including two novel variables, using the down sampled Landsat bands. These indices are then related to the local coincident SMAP values to identify a series of rules or trees to identify the local rules defining the relationship between soil moisture and the indices. The defined rules are then applied to the Landsat image in the native Landsat resolution to determine local soil moisture. Ground truth comparison is done via a series of grids using point soil moisture samples and air-borne L-band Multibeam Radiometer (PLMR) observations done under the SMAPEx-5 campaign. This paper uses a random forest due to its highly accurate learning against local ground truth data yet easily understandable rules. The predictive power of the inferred learning soil moisture algorithm did well with a mean absolute error of 0.054 over an airborne L-band retrieved surface over the same region.

Field experiments were carried out during 2011-2014 at the Research and Experimental Center to Combating Desertification located in the Elsen Tasarkhai station of central Mongolia. The study was aimed to identify the effects of oxidized brown coal humic acid fertilizer on the relative growth rate of several trees for forest rehabilitation. The trees used were Populus sibirica, Salix ledebouriana and Acer tataricum. The experiment was carried out with the concentrations of 2,000, 10,000 and 20,000 mg L-1 of humic acid fertilization treatments. The measurement of relative height growth rate (RHGR) was conducted for a period of 4 years. The treatments showed significant differences within humic fertilizer concentrations which differed depending on the species. Compared to the monthly RHGR over the study, the treatment of fertilizers were significantly better for tree growth. P. sibirica in the 10,000 and 20,000 mg L-1 humic acid fertilizers had significant height growth rates. In addition, when the humic acid treatments were compared to control, A. tataricum decreased over the years which were statistically significant for high growth rate and a positive effect of humic acid fertilization treatments was observed. Furthermore, results showed that oxidized brown coal humic acid fertilizers as organic fertilizer can have a positive effect for the growth of A. tataricum during the study years. The results showed that the soil chemical properties EC, CO2, NO3, and K2O were significant among all the treatments compared to the control. The effect on P2O5 statistically significantly increased among all the treatments. However, pH and Mg were not significant effect among all the treatments. Combining the results obtained from the oxidized brown coal humic acid fertilization with sustainable land management practices can help improve soil organics for environmental issues in degraded sandy soil regions.

Subseasonal-to-seasonal (S2S) weather forecasting has improved in recent years, thanks partly to better representation of physical variables in models. For instance, realistic initializations of snow and soil moisture in models yield enhanced predictability on S2S time scales. Snow depth and soil moisture also mediate month-to-month persistence of near-surface air temperature. Here the role of snow depth as predictor of temperature one month ahead in the Northern Hemisphere is probed via two causal pathways. Through the first pathway, snow depth anomalies in month 1 cause snow depth anomalies in month 2, which then cause temperature anomalies in month 2. This pathway represents the snow–albedo feedback, as well as cooling due to insulation, emissivity and heat loss. It is active from fall to summer, and its effect peaks in March/April in the midlatitudes and in May/June at high latitudes. A complementary second pathway, where snow depth anomalies in month 1 cause soil moisture anomalies in month 2, which then cause temperature anomalies in month 2 through soil moisture–temperature feedbacks, is only active in spring and summer. Its effect peaks later in the warm season than the effect of the first pathway. Geographically, snow depth mediates north of, and soil moisture south of, the areas with the highest temperature predictability from snow depth. These results indicate that the two pathways describe complementary physical mechanisms. The first pathway embodies month-to-month persistence of snow depth, and the second pathway represents melting of snow from one month to the next.

To enhance pomegranate production on marginal gravelly lands, standardized planting techniques were evaluated in an 8-year-old orchard. Trenching, wider pit excavation, pit digging, and auger digs with dimensions of 1 and 2 m were employed. Utilizing native soil from barren land, with or without spent wash, and mixing it with black soil up to 1 m deep, growth parameters, leaf nutrients, fruit production, and fruit quality were assessed. The trench and wider pit methods outperformed others, yielding greater above-ground biomass (>70.3 kg tree⁻¹), root biomass (>24.5 kg, tree⁻¹), and cross-sectional area (>3.30 m² tree⁻¹). These methods also produced longer roots (>4.0 m tree⁻¹) and higher leaf phosphorus (>0.28%) and potassium (>1.81%) levels, fruit juice content (>48.50%), and total soluble solids (>16.05°) compared to other planting methods. This resulted in higher and more sustainable fruit yield production under the trench and wider pit planting methods (>7.21 t ha⁻¹). Similarly, the native and black soil mixture produced healthy fruit trees, improved fruit quality, and sustainably higher fruit yield over the native soil alone. In summary, the trench and wider pit methods (2–3 m³), combined with a soil mixture, are recommended for sustainable, high-quality fruit production in shallow gravelly terrains, thereby improving food security and the livelihoods of farmers in arid regions.

Many papers in recent years have been devoted to estimating the per pixel proportions of three broad classes of materials (e.g. photosynthetic vegetation, non-photosynthetic vegetation and bare soil) using data from multispectral sensors. Many of these papers use estimation methods based on the linear mixture model. Very few of these papers assess the accuracy of their estimators. I show how to produce confidence intervals (CIs) and joint confidence regions (JCRs) for the proportions associated with various linear mixture models. There are two main models, both of which assume that the coefficients in the model are non-negative. The first model assumes that the coefficients sum to 1. The second does not, but uses rescaling of the estimated coefficients to produce estimated proportions. Three variants of these two models are also analysed. JCRs are shown to be particularly informative, because they are typically better at localising the information than CIs are. The methodology is illustrated using examples from Landsat Thematic Mapper data at 1169 locations across Australia, each of which has associated field observations. There is also discussion about the extent to which the methodology can be extended to hyperspectral data.

In this article, a method for the moisture mapping of the soil surface of agrophytocenosis was proposed using neural network based on synchronized radar and multispectral optoelectronic data of Sentinel-1,2. To verify the developed method, data from two experimental plots were used. These plots were located two on irrigated soybean crops. The first of them was located on the right bank (1st plot) and the second one on the left bank (2nd plot) of the down part of Volga River. Two experimental soil moisture geo-datasets were done by measurements and geo-referencing points using gravimetric method (1st plot) and with proximal sensing method (2nd plot) using Soil Moisture Sensor ML3-KIT (THETAKIT, Delta). The soil moisture retrieval algorithm was based on the use of a neural network to predict reflection coefficient of an electromagnetic wave from the soil surface, followed by inversion into soil moisture using a dielectric model that takes into account the soil texture. The input parameter of the neural network was the ratio of the microwave radar vegetation index (calculated on the basis of Sentinel-1 data) to the index (calculated on the basis of data of multispectral optoelectronic channels 8 and 11 of Sentinel-2). Such way calculated index reveals showed a significantly greater dependence on soil moisture than on vegetation height that was been used in previous studies. The retrieved values of soil moisture were compared with the soil moisture measured in-situ. The proposed method with a determination coefficient of 0.44-0.65 and a standard deviation of 2.4%-4.2% for the 1st plot as well as with and of the same metrics for the 2nd allows predicting the soil moisture of both a test plots covered by soybean plants, relative to soil moisture measured in-situ. The conducted research created the scientific basis for a new technology for remote sensing the moisture content of the soil surface of agrophytocenosis as an element of the precision farming system and agroecology.

As global concerns about climate change intensify, the need for effective strategies to reduce carbon emissions has never been more urgent. This paper explores the crucial role of data automation (DA) and decision support systems (DSS) in enhancing decarbonation efforts within the realms of solid waste management (SWM), wastewater treatment (WWT), and contaminated soil remediation (CSR). Specifically, the paper provides: (i) an overview of the carbon footprint (CFP) in relation to environmental management (EM) and the role of DA and DSS in decarbonization; (ii) DA case studies in areas of SWM, WWT, and CSR; (iii) 3. life cycle assessment (LCA) based DSS case studies in areas of SWM, WWT, and CSR; (iv) multi-criteria decision analysis (MCDA) based DSS case studies in areas of SWM, WWT, and CSR; and (v) optimal contractual delivery methods based DSS case studies in EM practices. The analysis disclosed that adoption of DA and DSS in SWM, WWT, and CSR holds significant potential for decarbonizing these EM processes. By optimizing operations, enhancing resource efficiency, and integrating renewable energy sources, these technologies contribute to the reduction of GHG emissions and promote sustainable environmental practices. As the demand for more effective and eco-friendly solutions grows, the role of DA and DSS will become increasingly pivotal in achieving global decarbonization goals.

1) Background: Climate change may subject forests to climate conditions to which they are not adapted. Elevated temperatures reduce net photosynthesis by increasing respiration rates and increasingly long droughts dramatically increase morbidity. CO₂ enrichment enhances productivity, but it is not clear to what extent CO₂ enrichment can offset the negative effects of elevated temperatures and longer droughts. 2) Methods: We used a mechanistic landscape model to conduct controlled simulation experiments manipulating CO₂ concentration, temperature, drought length and soil water capacity. 3) Results: We found that elevated CO₂ stimulates productivity such that it dwarfs the negative effect caused by elevated temperature. Energy reserves were not as strongly mitigated by elevated CO₂, and mortality of less competitive cohorts increased. Drought length had a surprisingly small effect on productivity measures, but had a marked negative effect on mortality risk. 4) Conclusions: Elevated CO₂ compensated for the negative effect of longer droughts in terms of productivity measures, but not survival measures.

Environment pollution is one of the major problem that faces by our civilization . Textile industry is one of the factor for pollute the environment. In this study various causes that occurs by textile industry and pollute the environment will be shown and described. In textile industry for energy to run the factory various kind of fuel are used and in the industry there are many chemicals and dyes are used for the textile products. Plastic is one of the main substance for making the synthetic fabric. They pollute the air, water, soil and also they pollute the ecosystem of the various animals and destroy the ozone surface of the earth. For various kind of pollution that causes by textile industry the life of normal life hampered. They got ill and many time men and women lost their organs and sometimes they got attacked by various life taking illness.

The objective of this study was to investigate the liquefaction resistance of chemically improved sandy soils in a straightforward and accurate manner. Using only the existing experimental databases and artificial intelligence, the goal was to make predictions without conducting physical experiments. Emphasis was placed on the significance of data from 20 loading cycles of cyclic undrained triaxial tests to determine the liquefaction resistance and the contribution of each explanatory variable. Different combinations of explanatory variables were considered. Regarding the predictive model, it was observed that a case with the liquefaction resistance ratio as the dependent variable and other parameters as explanatory variables yielded favorable results. In terms of exploring combinations of explanatory variables, it was found advantageous to include all variables as doing so consistently resulted in a high coefficient of determination. The inclusion of the liquefaction resistance ratio in the training data was found to improve the predictive accuracy. In addition, the results obtained when using a linear model for the prediction suggested the potential to accurately predict the liquefaction resistance using historical data.

In recent decades, large-scale forest disturbances such as windthrow and bark beetle infestations have significantly impacted the carbon balance of Earth and forest ecosystems, mainly by altering soil respiration (SR) in addition to decreased gross primary productivity. SR is considered the second highest flux on Earth, emitting 78-98 Gt C yr-1 to the atmosphere, thus contributing to global warming. Post-disturbance development is marked by changes in the size of SR. To investigate the impact of bark beetle (Ips typographus) infestations in mountain spruce forests on SR, we measured SR at infested sites by bark beetles and adjacent undisturbed stands in the High Tatra Mountains (Slovakia). The measurements were conducted during the vegetation period (May-September) in 2016 and 2017, five to six years after the initial attack by bark beetles, at an altitudinal gradient (1100-1400 m a.s.l.). SR varied throughout different months and altitudes, and we observed that SR at infested sites reached higher values (in most cases) than at uninfested stands. Average monthly and altitude SR rates during both vegetation periods showed higher values at infested sites, but not all of them were statistically significant. We observed an insignificant change in SR rate between 2016 and 2017 at both infested and uninfested sites. The highest rates of SR were observed in 2016 from July to August at both infested and undisturbed sites. On the other hand, in 2017 the highest SR rates were observed in June and July at undisturbed sites and from June to August at infested sites. However, yearly SR from May-September at infested sites showed significantly higher rates than uninfested ones in both years. SR showed a decreasing pattern with elevation gain in 2016 at infested sites. However, this pattern was not observed in 2017. Based on our observation and results we postulate that SR rates at infested sites are boosted by needle fall and debris from dead and dying trees, which increases heterotrophic SR and compensates the decrease of autotrophic SR from tree roots. This study provides important insights into the impact of bark beetle infestations on SR and highlights the need for further research on the long-term effects of forest disturbances on carbon cycling.

Saline water irrigation contributes significantly to forage yield. However, the salinity threshold for safe saline water irrigation of tall wheatgrass in coastal saline-alkaline land remains unclear. In this study, 2 g L‒1, 3 g L‒1, 4 g L‒1, 5 g L‒1, and 8 g L‒1 saline waters were used for irrigation. Two irrigations with 2+3 g L‒1 saline waters produced the highest yield, followed by one irrigation with 4 g L‒1 or 5 g L‒1 saline water. After rainfall’s leaching, the soil electrical conductivity (EC1:5) reduced by 41.7%‒79.3% for the saline water irrigation treatments. In combination with saline water irrigation, plastic film mulching can be used for sward establishment of seed propagated tall wheatgrass. However, irrigation with high salinity of drainage water enhanced the risk of plant death. Further, a pot experiment demonstrated that irrigation with 5 g L‒1 saline water led to the least reduction of forage yield and the highest crude protein content in leaves. However, the plants irrigated with ≥7 g L‒1 saline water enhanced soil salinity and reduced plant height, leaf size, and gas exchange rate. Conclusively, one irrigation with ≤5 g L‒1 saline water at the end of April or early May could be acceptable to maximize the forage yield of tall wheatgrass and minimize soil salinization risk in the coastal saline-alkaline land around the Bohai Sea.

The objective of this study is to investigate the use of excavation behavior prediction and analysis software in the field of civil en-gineering. PLAXIS and SoilWorks, widely used in the engineering and academic communities, were employed as research tools for analyzing gravel layers and deriving appropriate ranges of soil elastic modulus for different software programs. By collecting excavation cases in the Xindian area of New Taipei City, Taiwan, and conducting deep excavation analysis using the displacement of retaining walls, this study provides recommended ranges for localized selection of soil elastic modulus. The suggested range for PLAXIS is 7,840 N/m² to 9,800 N/m², while Soil-Works suggests a range of 2,450 N/m² to 3,430 N/m². These ranges allow for reasonable estimation of maximum deformations during the final excavation stage. Based on the research findings, it is recommended that engineers refer to the provided ranges when selecting soil elastic modulus for excavation analysis in gravel layers using different soft-ware programs in the Xindian area of Taiwan to improve the accuracy of deformation predictions during the final excavation stage. These results serve as important references for engineers and contribute to the effective prediction and management of excavation behavior in civil engineering projects.

Human leptospirosis cannot be investigated without studying zoonotic and environmental as-pects of the disease. The objectives of this study are to explore the abundance of Leptospira in dif-ferent climate zones of Sri Lanka and to describe the presence of Leptospira in same water source at different time points. First, water and soil samples were collected from whole-island, secondly, water sampling continued only in dry-zone, finally serial sampling of water from ten open wells was performed at five different time points. Quantitative PCR for water and metagenomic se-quencing for soil were used to detect Leptospira. In first component, 2 out of 12 water sites were positive and both are situated in wet-zone. Very small quantities of Genus Leptospira was detect-ed by metagenomic analysis of soil. Only 5 out of 26 samples were positive in the second compo-nent. Six, five, four, five, six wells were positive respectively in serial measurements of third component. All wells were positive at least one measurement while only one well was positive in all measurements. Closer to tank and higher distance from main road were significant risk fac-tors associated with well positivity. Presence of Leptospira seems not consistent indicating ran-dom abundance of Leptospira in natural environment.

Here we evaluate the chemistry of mineral soil solutions collected by suction lysimeters in a small mountain catchment that was affected by acidification-related spruce die-back. The aim was to obtain new insights into spatial patterns of nutrient partitioning during acidification recovery. This was achieved by comparing nutrient concentrations in soil solutions, collected along a V-shaped valley, with those in runoff. Five nests of suction lysimeters were installed in the 33-ha UDL catchment at different topographic positions (hilltops, slopes and valley). Following a 1-year equilibration, monthly samples of soil solutions were collected over a 2-year period. In the vicinity of each lysimeter nest, soil pits were excavated for a study of soil chemistry. Soil solutions were analyzed for SO42-, NO3-, DOC, NH4+, Na+, K+, Ca2+, Mg2+, and total dissolved Al concentrations, DOC and pH. For a P release estimation, ammonium oxalate extraction of soil samples was performed. Comparison of soil water data from this study with other European studies indicated that major environmentally relevant chemical species at UDL had concentrations similar to median concentrations across numerous previously acidified sites. CEC (≤ 58 meq kg-1) and BS (≤ 13 %), however, were significantly lower at UDL than at other European sites, documenting incomplete recovery from acidification. Spatial trends and seasonality in soil water chemistry support belowground inputs from mineral-stabilized legacy pollutants and soil nutrients. Seasonal variability of soil water sulfate and nitrate was significant. High nitrate in soil solutions during summer originated during the preceding dormant season, and high sulfate concentrations observed during the winter originating from recycled organic S during the summer. Higher concentrations of SO42-, NO3-, and base cations in runoff than in soil solutions may be explained by lateral surficial runoff leaching pollutants and nutrients from very shallow soil horizons. Nearly 30 years after peak acidification, solutions in mineral subsoil at UDL exhibited similar concentrations of SO42-, Ca2+ and Mg2+ as median values at the Pan-European International Co-operative Program (ICP) Forest sites, yet NO3- concentrations were an order of magnitude higher than the ICP sites. Calcium applied on soil surface by liming 32 to 11 years ago affected runoff more than soil solutions, suggesting contribution of both shallow soil water and groundwater to runoff.

Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems, enhancing the understanding hydrological processes, optimizing the allocation and distribution of water resources, and assessing, forecasting and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors or satellite observations. These data are utilized in describing both small and large scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically evolve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air- and space-borne remote sensing, by providing not just high spatial detail over relatively large areas in a cost-effective way, but as importantly providing an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and applications specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, post-processing techniques, retrieval algorithms and evaluations techniques need to be harmonized. The aim of this paper is to provide a comprehensive general overview of the existing research on studies and applications of UAS in environmental monitoring in order to suggest users and researchers on future research directions, applications, developments and challenges.

This note makes available five years of data gathered in a measurement site equipped with a micrometeorological station and two monitoring wells. Series of data of hydrological and atmospheric variables allow to estimate the flux of water across the atmosphere-land interface and to calculate the water budget, that are crucial topics in climate and environmental sciences. The measures of water-table began during 2017, one of the driest year of the whole instrumental period of climate history for the Central Mediterranean. Data from the micrometeorological station have been used to construct two more datasets of daily and monthly cumulative values of different terms of the surface water budget, from which the groundwater infiltration has been estimated. A significant decreasing trend characterizes both the data time series of piezometric heights and estimated infiltration in the considered period. All the data are collected in 2017-2022 datasets that are available in the supplementary material.

Increased nutrient withdrawal by rapidly expanding intensive cropping systems, in combination with imbalanced fertilization, is leading to potassium (K) depletion from agricultural soils in Asia. There is an urgent need to better understand the soil K-supplying capacity and K-use efficiency of crops to address this issue. Maize is increasingly being grown in rice-based systems in South Asia, particularly in Bangladesh and North East India. The high nutrient extraction, especially K, however, causes concerns for the sustainability of maize production systems in the region. The present study was designed to estimate, through a plant-based method, the magnitude, and variation in K-supplying capacity of a range of soils from the maize-growing areas and the K-use efficiency of maize in Bangladesh. Eighteen diverse soils were collected from several upazillas (or sub-districts) under 11 agro-ecological zones to examine their K-supplying capacity from the soil reserves and from K fertilization (@ 100 mg K kg-1 soil) for successive seven maize crops grown up to V10-V12 in pots inside a net house. A validation field experiment was conducted with five levels of K (0, 40, 80, 120 and 160 kg ha-1) and two fertilizer recommendations based on “Nutrient Expert for Maize-NEM” and “Maize Crop Manager-MCM” decision support tools (DSSs) in 12 farmers’ fields in Rangpur, Rajshahi and Comilla districts in Bangladesh. Grain yield and yield attributes of maize responded significantly (P < 0.001) to K fertilizer, with grain yield increase from 18 to 79% over control in all locations. Total K uptake by plants not receiving K fertilizer, considered as potential K-supplying capacity of the soil in the pot experiment, followed the order: Modhukhali >Mithapukur >Rangpur Sadar >Dinajpur Sadar >Jhinaidah Sadar >Gangachara >Binerpota >Tarash >Gopalpur >Daudkandi >Paba >Modhupur >Nawabganj Sadar >Shibganj >Birganj >Godagari >Barura >Durgapur. Likewise in the validation field experiment, the K-supplying capacity of soils was 83.5, 60.5 and 57.2 kg ha-1 in Rangpur, Rajshahi, and Comilla, respectively. Further, the order of K-supplying capacity for three sites was similar to the results from pot study confirming the applicability of results to other soils and maize-growing areas in Bangladesh and similar soils and areas across South Asia. Based on the results from pot and field experiments, we conclude that the site-specific K management using the fertilizer DSSs can be the better and more efficient K management strategy for maize.

The 2020 Elucidating the role of clouds-circulation coupling in climate - Ocean-Atmosphere (EUREC4A-OA) and Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) campaigns sought to improve the knowledge of the interaction between clouds, convection and circulation and their function in our changing climate. The campaign consisted of numerous research technologies, some of which are relatively novel to the scientific community. In this study we used a saildrone uncrewed surface vehicle to validate satellite and modelled sea surface salinity (SSS) products in the Western Tropical Atlantic. These products include the Soil Moisture Active Passive (SMAP) Jet Propulsion Laboratory (JPL), SMAP Remote Sensing Systems (RSS), and Hybrid Coordinate Ocean Model (HYCOM). In addition to the validation, we investigated a fresh tongue south east of Barbados. The saildrones accurately depicted the salinity conditions and all satellite and modelled products performed well in areas that lacked small-scale salinity variability. However, SMAP RSS 70 km outperformed its counterparts in areas with small submesoscale irregularities while RSS 40 km was better at identifying small irregularities in salinity such as a fresh tongue. These results will allow researchers to make informed decisions regarding the most ideal product for their application and aid in the improvement of mesoscale and submesoscale SSS products, which can lead to the refinement of numerical weather prediction (NWP) and climate models.

This paper introduces an innovative approach to numerically model Structure-Soil-Structure Interaction (SSSI) by integrating the Boundary Element Method (BEM) and the Finite Element Method (FEM) in a coupled manner. To assess the accuracy of the proposed method, a comparative study is undertaken, comparing its outcomes with those generated by the conventional FEM technique. Alongside accuracy, the computational efficiency aspect is crucial for the analysis of large-scale SSSI problems. Hence, the computational performance of the coupled BEM-FEM method undergoes a thorough examination and is compared with that of the standalone FEM method. The results from these comparisons illustrate the superior capabilities of the proposed method in comparison to the FEM method. The novel approach provides more reliable results compared to traditional FEM methods, serving as a valuable tool for engineers and researchers involved in structural analysis and design.

The aim of the study was to determine the effect of the application of different organic matter, UGmax soil conditioner and simplifications in potato cultivation on the content of dry matter, starch, sugars in tubers of the medium-early edible cultivar 'Satina' after harvest and after long-term storage. The highest dry matter (173.4 g kg-1) and starch (124.6 g kg-1 f. m.) content was ob-tained with the simultaneous application of a manure with soil conditioner at 100% mineral fer-tilization. In the case of sugars, the withdrawal of the soil conditioner from the crop proved most beneficial, for total sugars on the stubble intercrop (5.06 g kg-1 f. m.) and for reducing sug-ars (1.99 g kg-1 f. m.) on the straw. Each protection reduction applied resulted in a significant re-duction in starch content. In this regard, the withdrawal of herbicides with simultaneous appli-cation of manure and UGmax proved most beneficial. Long-term storage of tubers caused a sig-nificant reduction in their quality in terms of dry matter and starch content (average by -3.6 and -2.3%, respectively) and an increase in total and reducing sugars (average by 11.8 and 9.6%, re-spectively). The decrease in dry matter and starch content was significantly influenced by the 50% reduction in NPK fertilization applied during the growing season, while the application of soil conditioner with full protection contributed to the increase in reducing sugars after storage at 28.9 pts%. Our research is in line with current trends of used potato cultivation technologies focused mainly on environmental protection, so the results of this study can provide a basis for validation for researchers currently engaged in such evaluation.

Water is one of the most critical factors affecting environmental health. For this reason, estimating and monitoring the behavior of water in nature and preventing water pollution before it occurs are advantageous to avoid problems that may occur in terms of environmental health. In order to predict the behavior of water, the hydrological cycle needs to be evaluated on a basin basis. At this point, hydrological models can be used to make mathematical representations of hydrologi-cal processes. These models play an essential role in predicting and monitoring problems such as poor water quality, pollution, particle and sediment transport, and proliferation of oil and petro-leum derivatives among frequently encountered issues in environmental health. In this study, a 2D surface water model was created using soil and water assessment tool (SWAT) to simulate the lotic ecosystem and existing water quality in Tatlıçay Basin and to propose solutions for im-proving environmental health in Cankiri provincial center in Türkiye. The accuracy of the input data and the validity of the model were checked with the calibration and validation studies by using the monthly or 3-monthly observation data of the General Directorate of State Hydraulic Works’ flow observation & water quality stations from 2016 to 2020. The aim was to provide the opportunity to produce fast, accurate, and practical solutions in the face of negative scenarios with this model. This modelling study obtained successful results in the calibration and valida-tion process with very few observed data. With this study, a significant contribution to the litera-ture on environmental health is made, as there aren't many examples of hydrological modeling from the Middle East.

Thermal treatment in Australia is gaining interest due to legislative changes, waste reduction goals, and the need to address contaminants risks in biosolids used for agriculture. The resulting biochar product has the potential to be beneficially recycled as a soil amendment. On-farm management practices were reviewed to identify barriers that need to be overcome to increase recycling and examine the role of pyrolysis and gasification in effectively improving the quality and safety of biochar. Key findings revealed: (1) thermal treatment can effectively eliminate persistent organic pollutants, microplastics and pathogens, and (2) more than 90% of the total heavy metals content in biosolids become immobilized when these are converted to biochar, thus reducing their bioavailability following land application. While reported research on the short-term effects of biosolids-derived biochar suggested promising agronomic results, there is dearth of information on long-term effects. Other knowledge gaps include optimisation of land application rates, understanding of rate of breakdown and fate of contaminants in soil and water, heavy metal mobility in soil and bioaccumulation or transfer to the food chain. Improved understanding of nutrients and contaminants dynamics in soils receiving biosolids-derived biochar is a pre-requisite for their safe use in Australian agriculture, and therefore it is highlighted as priority area for future research.

With global warming and accelerated degradation of permafrost, the engineering problems caused by the formation of weak zones between the shallow and permafrost layers of soil–rock mixture (S-RM) slopes in permafrost regions have become increasingly prominent. To explore the influence of rock content on the shear strength of the S-RM freezing–thawing interface, the variation in the shear strength for different rock content is studied herein using direct shear tests. In addition, a 3D laser scanner is used for obtaining the topography of the shear failure surface. Combined with the analysis results of the shear band-particle calculation model, the influence of the rock content on the shear strength of the interface is explored. It was found that the impact threshold of the rock content on the interface strength and failure mode is approximately 30%, when the rock content (R) is &gt; 30% and that the shear strength increases rapidly with increasing rock content. When R ≤ 30%, the actual shear plane is similar to waves; when R &gt; 30%, the shear plane appears as gnawing failure. The shear strength of S-RM freezing–thawing interface mainly comes from the bite force and friction between particles. The main reason for the increase in shear strength with increasing rock content is the increase in bite force between particles, which makes the ratio of bite force to friction force approximately 1:1.

A saprophytic soil fungus, Aspergillus flavus, produces aflatoxin (toxigenic strains) in the kernels of corn (Zea mays L.) and seeds of many other crops. Many strains of A. flavus do not produce toxigenic aflatoxin, and soil application of these atoxigenic strains is a suppressive control tactic to assist in controlling toxigenic conspecifics. Effects of atoxigenic A. flavus applications on honey bees (Apis mellifera L.) and other bees are unknown, and basic information on bee occurrences in corn fields treated with and without this biological pesticide is needed to inform integrated pest management in corn. Fields with atoxigenic A. flavus applications were compared to nearby control fields in three counties in corn production regions in eastern Texas. In each corn field, twenty bee bowl traps were deployed along four equal transects located between corn rows, with contents of the bowls (i.e. bees) retrieved after 24 hours. Eleven bee genera from four families were collected from corn fields, with only two honey bees collected and zero honey bees observed in transects. The sweat bee genus Agapostemon (primarily composed of the Texas-striped sweat bee A. texanus) was most abundant in corn fields (44% of the total number of bees collected) followed by long-horned bees (Melissodes spp., 24%). The southernmost county (i.e. San Patricio) produced over 80% of the total number of bees collected. Bee communities occurring in corn production fields with applications of atoxigenic A. flavus applications were not significantly different from nearby control fields. While little is known of bee resource use in corn production systems in Texas, the abundant yet variable bee communities across latitudes in this study suggests a need to investigate the influence of farming practices on bee resources in regional corn production systems.

In this study, the effects of food waste (FW) compost, vermicompost, and chemical fertilizers on the growth of organic red radish and soil quality are investigated to promote sustainable agriculture. Utilizing a comparative approach, it assesses the impacts of these fertilizers on various plant growth metrics and soil characteristics, highlighting the potential of converting FW into nutrient-rich amendments as a sustainable alternative to chemical fertilizers. The results indicate a significant variation in fresh weight among different treatments. For example, the FV 25% treatment demonstrated a relatively high mean fresh weight of 7.9664, while the CO 50% treatment yielded a much lower mean fresh weight of 0.2589. These numbers underscore the potential efficacy of specific FW treatments in enhancing plant growth, with VC 50% and FV 25% showing considerable promise in increasing crop yield. The study concludes that FW compost and vermicompost significantly enhance plant growth and soil quality, advocating for their use as sustainable and environmentally friendly alternatives to chemical fertilizers. The findings emphasize the importance of selecting appropriate fertilizer types and concentrations to optimize agricultural productivity and environmental sustainability, supporting the incorporation of FW into agricultural systems as a beneficial resource.

In India, particularly within its North-Eastern territories, the occurrence of landslides triggered by post-dry period rainfall events is a significant concern, with the phenomena persistently causing extensive damage to both life and infrastructure. Despite the inevitability of such natural disasters, it is understood that the impact they exert can be substantially mitigated through the implementation of pre-emptive measures. Critical to the endeavour of landslide prevention is the application of a spectrum of mitigation strategies, predicated upon a foundational analysis of slope stability to ascertain critical surfaces for intervention. This investigation delves into the dynamics of partially saturated soils, a condition commonly resultant from the prolonged dry periods preceding the monsoonal rains. The study's focal point is an experimental examination conducted on soil samples to assess parameters such as soil matric suction and unconfined compressive strength across varying degrees of saturation and density, alongside performing analyses of slope failure incidents after dry period during the 2017 monsoon in the Lunglei district, Mizoram based on some established data. The Soil-Water Characteristic Curve (SWCC), derived from soil suction measurements employing the ASTM D5298-10 standard, utilizes a contact filter paper method and serves to delineate the moisture transition phases of the slope. To replicate the desiccation of the soil slope after monsoonal rains, a microwave drying technique based on the ASTM standard was employed to prepare unsaturated soil samples for Unconfined Compressive Strength (UCS) testing. The integrity of the slope was evaluated in a temporal context, before and after rainfall events of varied intensities throughout the calendar year, utilizing a blend of numerical modeling and empirical laboratory investigations. The persistence of real onsite slope failures underscores the necessity for a numerical analysis approach to unravel the underlying factors causing slope instability, especially given the absence of prior stability assessments for the landslide in question. An evaluation of SWCC curves, UCS tests on partially saturated soil at different densities and saturation and the utilization of the benchmark data collected in 2017 on the shallow transitional landslide in Lunglei, Mizoram caused by intense monsoonal precipitation, are central key data and information used to carry out this research. Rainfall data for Lunglei, sourced from the Mizoram Meteorological Department, facilitated a real-time monsoonal data-driven numerical analysis. This analysis elucidated the pivotal role of rainfall in inducing slope failure, indicating a marked destabilization of the slope after precipitation events. The implications of these findings are far-reaching, suggesting a foundational basis for the development of an advanced landslide early warning system, thereby enhancing disaster preparedness, and mitigating potential damages.

All empirical water column correction methods have consistently been reported to require existing depth sounding data for the purpose of calibrating a simple depth retrieval model; they yield poor results over very bright or very dark bottoms. In contrast, we set out to (i) use only the relative radiance data in the image along with published data, and several new assumptions, (ii) in order to specify and operate the simplified radiative transfer equation (RTE), (iii) for the purpose of retrieving both the satellite derived bathymetry (SDB) and the water column corrected spectral reflectance over shallow seabeds. Sea truth regressions show that SDB depths retrieved by the method only need tide correction. Therefore it shall be demonstrated that, under such new assumptions, there is no need (i) for formal atmospheric correction, (ii) nor for conversion of relative radiance into calibrated reflectance , (iii) nor for existing depth sounding data, to specify the simplified RTE and produce both SDB and spectral water column corrected radiance ready for bottom typing. Moreover, the use of the panchromatic band for that purpose is introduced. Altogether, we named this process the Self-Calibrated Supervised Spectral Shallow-sea Modeler (4SM). This approach requires a trained practitioner, though, to produce its results within hours of downloading the raw image. The ideal raw image should be a “near-nadir” view, exhibit homogeneous atmosphere and water column, include some coverage of optically deep waters and bare land, and lend itself to quality removal of haze, atmospheric adjacency effect, and sun/sky glint.

This study proposes a flexible and adaptable protocol for the establishment of agricultural management zones that utilises remote sensing, ground truthing (apparent electrical conductivity and soil sampling), the IRRIGOPTIMAL® system and machine learning. This protocol could contribute significantly to the rational use of inputs (water, fertilizers and pesticides) and to the further efficient development of an optimal irrigation system with variable irrigation rates. The methodology to develop this protocol was applied to olive and alfalfa plots in Heraklion (Crete, Greece) to monitor soil and crop responses for the period 2022-2024. Spatial and temporal assessment of selected soil and plant parameters (moisture, photosynthetic activity) using ground and vegetation reflectance mapping by satellites and unmanned aerial systems provides important information in both the pre- and main phases of the management zone delineation. Geophysical methods such as electromagnetic induction, applied in the main phase of management zone delineation, provide a robust technique to determine the spatial and temporal distribution of apparent electrical conductivity. The Random Forest machine learning model was found to be most suitable for predicting soil electrical conductivity based on satellite-derived salinity indices and ground electromagnetic induction. Finally, the IRRIGOPTIMAL® system provides real-time monitoring of a variety of weather and soil parameters to determine the optimal cultivation of crops based on the creation of agricultural management zones.

Seasonal precipitation variance significantly alters soil water content, potentially inducing water stress and affecting plant transpiration in semiarid deserts. This study explored the effects of environmental variables and hydraulic conductance on whole-tree transpiration (ET) in Mongolian pines (Pinus sylvestris var. mongolica) across different forest stages in the semiarid deserts of northern China. We measured ET using sap flow in mature (MMP), half-mature (HMP), and young (YMP) Mongolian pine plantations. Measurements included soil-leaf water potential difference (ΔΨ), atmospheric conditions, and soil moisture contents on sunny days, both in dry and wet periods. Seasonally variable rainfall distinctly affected soil moisture, correlating ET with photosynthetically active radiation (PAR) and vapor pressure deficit (VPD). Notable nocturnal sap flow occurred during the growing season. During the dry periods, both stomatal and hydraulic conductance influenced ET, whereas during the wet periods, stomatal conductance primarily regulated it. Discrepancies between predicted and measured ET were noticed: compared to the predicted ET, measured ET was lower during dry periods while higher during wet periods. Hydraulic conductance (KT) increased with tree height (H) and ΔΨ, although tall trees exhibited lower KT rates, suggesting that hydraulic compensation had occurred. This compensation, observed between 11:00 and 13:00, aligned with increased hydraulic resistance during dry periods. Decreasing hydraulic conductance intensified leaf water stress in dry periods, especially during the time when PAR and VPD were heightened, potentially increasing stomatal sensitivity to drought, promoting water conservation and plant survival. A linear relationship between predawn and midday leaf water potentials was noticed, indicating extreme anisohydric behavior across forest stages during both dry and wet periods. Although both stomatal and hydraulic conductance influenced ET during the dry period, MMP and YMP were more susceptible to drought conditions. Understanding these dynamics could help evaluate semiarid desert ecological functions for water conservation amidst uneven seasonal precipitation in Northern China.